{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Improving Word2vec Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\thushan\\documents\\python_virtualenvs\\tensorflow_venv\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "%matplotlib inline\n",
    "##from __future__ import print_function\n",
    "import collections\n",
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "import bz2\n",
    "from matplotlib import pylab\n",
    "from six.moves import range\n",
    "from six.moves.urllib.request import urlretrieve\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.cluster import KMeans\n",
    "import nltk # standard preprocessing\n",
    "##import operator # sorting items in dictionary by value\n",
    "#nltk.download() #tokenizers/punkt/PY3/english.pickle\n",
    "from math import ceil\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "This code downloads a [dataset](http://www.evanjones.ca/software/wikipedia2text.html) consisting of several Wikipedia articles totaling up to roughly 61 megabytes. Additionally the code makes sure the file has the correct size after downloading it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found and verified wikipedia2text-extracted.txt.bz2\n"
     ]
    }
   ],
   "source": [
    "url = 'http://www.evanjones.ca/software/'\n",
    "\n",
    "def maybe_download(filename, expected_bytes):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  if not os.path.exists(filename):\n",
    "    filename, _ = urlretrieve(url + filename, filename)\n",
    "  statinfo = os.stat(filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified %s' % filename)\n",
    "  else:\n",
    "    print(statinfo.st_size)\n",
    "    raise Exception(\n",
    "      'Failed to verify ' + filename + '. Can you get to it with a browser?')\n",
    "  return filename\n",
    "\n",
    "filename = maybe_download('wikipedia2text-extracted.txt.bz2', 18377035)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read Data with Preprocessing with NLTK\n",
    "Reads data as it is to a string, convert to lower-case and tokenize it using the nltk library. This code reads data in 1MB portions as processing the full text at once slows down the task and returns a list of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading data...\n",
      "Data size 3360286\n",
      "Example words (start):  ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing']\n",
      "Example words (end):  ['favorable', 'long-term', 'outcomes', 'for', 'around', 'half', 'of', 'those', 'diagnosed', 'with']\n"
     ]
    }
   ],
   "source": [
    "def read_data(filename):\n",
    "  \"\"\"\n",
    "  Extract the first file enclosed in a zip file as a list of words\n",
    "  and pre-processes it using the nltk python library\n",
    "  \"\"\"\n",
    "\n",
    "  with bz2.BZ2File(filename) as f:\n",
    "\n",
    "    data = []\n",
    "    file_size = os.stat(filename).st_size\n",
    "    chunk_size = 1024 * 1024 # reading 1 MB at a time as the dataset is moderately large\n",
    "    print('Reading data...')\n",
    "    for i in range(ceil(file_size//chunk_size)+1):\n",
    "        bytes_to_read = min(chunk_size,file_size-(i*chunk_size))\n",
    "        file_string = f.read(bytes_to_read).decode('utf-8')\n",
    "        file_string = file_string.lower()\n",
    "        # tokenizes a string to words residing in a list\n",
    "        file_string = nltk.word_tokenize(file_string)\n",
    "        data.extend(file_string)\n",
    "  return data\n",
    "\n",
    "words = read_data(filename)\n",
    "print('Data size %d' % len(words))\n",
    "token_count = len(words)\n",
    "\n",
    "print('Example words (start): ',words[:10])\n",
    "print('Example words (end): ',words[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Dictionaries\n",
    "Builds the following. To understand each of these elements, let us also assume the text \"I like to go to school\"\n",
    "\n",
    "* `dictionary`: maps a string word to an ID (e.g. {I:0, like:1, to:2, go:3, school:4})\n",
    "* `reverse_dictionary`: maps an ID to a string word (e.g. {0:I, 1:like, 2:to, 3:go, 4:school}\n",
    "* `count`: List of list of (word, frequency) elements (e.g. [(I,1),(like,1),(to,2),(go,1),(school,1)]\n",
    "* `data` : Contain the string of text we read, where string words are replaced with word IDs (e.g. [0, 1, 2, 3, 2, 4])\n",
    "\n",
    "It also introduces an additional special token `UNK` to denote rare words to are too rare to make use of."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most common words (+UNK) [['UNK', 69215], ('the', 226881), (',', 184013), ('.', 120944), ('of', 116323)]\n",
      "Sample data [1721, 9, 8, 16822, 223, 4, 5167, 4479, 26, 11851]\n"
     ]
    }
   ],
   "source": [
    "# we restrict our vocabulary size to 50000\n",
    "vocabulary_size = 50000 \n",
    "\n",
    "def build_dataset(words):\n",
    "  count = [['UNK', -1]]\n",
    "  # Gets only the vocabulary_size most common words as the vocabulary\n",
    "  # All the other words will be replaced with UNK token\n",
    "  count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
    "  dictionary = dict()\n",
    "\n",
    "  # Create an ID for each word by giving the current length of the dictionary\n",
    "  # And adding that item to the dictionary\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "    \n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  # Traverse through all the text we have and produce a list\n",
    "  # where each element corresponds to the ID of the word found at that index\n",
    "  for word in words:\n",
    "    # If word is in the dictionary use the word ID,\n",
    "    # else use the ID of the special token \"UNK\"\n",
    "    if word in dictionary:\n",
    "      index = dictionary[word]\n",
    "    else:\n",
    "      index = 0  # dictionary['UNK']\n",
    "      unk_count = unk_count + 1\n",
    "    data.append(index)\n",
    "    \n",
    "  # update the count variable with the number of UNK occurences\n",
    "  count[0][1] = unk_count\n",
    "  \n",
    "  reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys())) \n",
    "  # Make sure the dictionary is of size of the vocabulary\n",
    "  assert len(dictionary) == vocabulary_size\n",
    "    \n",
    "  return data, count, dictionary, reverse_dictionary\n",
    "\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10])\n",
    "del words  # Hint to reduce memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Batches of Data for Skip-Gram\n",
    "Generates a batch or target words (`batch`) and a batch of corresponding context words (`labels`). It reads `2*window_size+1` words at a time (called a `span`) and create `2*window_size` datapoints in a single span. The function continue in this manner until `batch_size` datapoints are created. Everytime we reach the end of the word sequence, we start from beginning. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data: ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed']\n",
      "\n",
      "with window_size = 1:\n",
      "    batch: ['is', 'is', 'a', 'a', 'concerted', 'concerted', 'set', 'set']\n",
      "    labels: ['propaganda', 'a', 'is', 'concerted', 'a', 'set', 'concerted', 'of']\n",
      "\n",
      "with window_size = 2:\n",
      "    batch: ['a', 'a', 'a', 'a', 'concerted', 'concerted', 'concerted', 'concerted']\n",
      "    labels: ['propaganda', 'is', 'concerted', 'set', 'is', 'a', 'set', 'of']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch_skip_gram(batch_size, window_size):\n",
    "  # data_index is updated by 1 everytime we read a data point\n",
    "  global data_index \n",
    "    \n",
    "  # two numpy arras to hold target words (batch)\n",
    "  # and context words (labels)\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    \n",
    "  # span defines the total window size, where\n",
    "  # data we consider at an instance looks as follows. \n",
    "  # [ skip_window target skip_window ]\n",
    "  span = 2 * window_size + 1 \n",
    "    \n",
    "  # The buffer holds the data contained within the span\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  \n",
    "  # Fill the buffer and update the data_index\n",
    "  for _ in range(span):\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  \n",
    "  # This is the number of context words we sample for a single target word\n",
    "  num_samples = 2*window_size \n",
    "\n",
    "  # We break the batch reading into two for loops\n",
    "  # The inner for loop fills in the batch and labels with \n",
    "  # num_samples data points using data contained withing the span\n",
    "  # The outper for loop repeat this for batch_size//num_samples times\n",
    "  # to produce a full batch\n",
    "  for i in range(batch_size // num_samples):\n",
    "    k=0\n",
    "    # avoid the target word itself as a prediction\n",
    "    # fill in batch and label numpy arrays\n",
    "    for j in list(range(window_size))+list(range(window_size+1,2*window_size+1)):\n",
    "      batch[i * num_samples + k] = buffer[window_size]\n",
    "      labels[i * num_samples + k, 0] = buffer[j]\n",
    "      k += 1 \n",
    "    \n",
    "    # Everytime we read num_samples data points,\n",
    "    # we have created the maximum number of datapoints possible\n",
    "    # withing a single span, so we need to move the span by 1\n",
    "    # to create a fresh new span\n",
    "    buffer.append(data[data_index])\n",
    "    data_index = (data_index + 1) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
    "\n",
    "for window_size in [1, 2]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch_skip_gram(batch_size=8, window_size=window_size)\n",
    "    print('\\nwith window_size = %d:' % window_size)\n",
    "    print('    batch:', [reverse_dictionary[bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Original Skip-Gram Algorithm\n",
    "The original skip-gram algorithm did not have a hidden layer but calculated the loss from the embeddings themselves. Therefore, skip-gram algorithm had two different embedding layers one for inputs and one for outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Hyperparameters\n",
    "\n",
    "Here we define several hyperparameters including `batch_size` (amount of samples in a single batch) `embedding_size` (size of embedding vectors) `window_size` (context window size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128 # Data points in a single batch\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "window_size = 4 # How many words to consider left and right.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors\n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "# We sample valid datapoints randomly from a large window without always being deterministic\n",
    "valid_window = 50\n",
    "\n",
    "# When selecting valid examples, we select some of the most frequent words as well as\n",
    "# some moderately rare words as well\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "valid_examples = np.append(valid_examples,random.sample(range(1000, 1000+valid_window), valid_size),axis=0)\n",
    "\n",
    "num_sampled = 32 # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Inputs and Outputs\n",
    "\n",
    "Here we define placeholders for feeding in training inputs and outputs (each of size `batch_size`) and a constant tensor to contain validation examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Training input data (target word IDs).\n",
    "train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "# Training input label data (context word IDs)\n",
    "train_labels = tf.placeholder(tf.int64, shape=[batch_size, 1])\n",
    "# Validation input data, we don't need a placeholder\n",
    "# as we have already defined the IDs of the words selected\n",
    "# as validation data\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Model Parameters and Other Variables\n",
    "We now define two TensorFlow variables as embedding layers(`in_embeddings` and `out_embeddings`). Note that we do not have any neural network parameters (`softmax_weights` and `softmax_biases`) as we had in the skip-gram algorithm code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables\n",
    "\n",
    "# Embedding layers, contains the word embeddings\n",
    "# We define two embedding layers\n",
    "# in_embeddings is used to lookup embeddings corresponding to target words (inputs)\n",
    "in_embeddings = tf.Variable(\n",
    "    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
    ")\n",
    "# out_embeddings is used to lookup embeddings corresponding to contect words (labels)\n",
    "out_embeddings = tf.Variable(\n",
    "    tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0)\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Model Computations\n",
    "\n",
    "Here we define several TensorFlow opeartions required for computing loss and predictions. We first defing an opeartion to fetch negative samples for a given batch of data. Next we define embedding lookup functions for both true (`in_embed` and `out_embed`) and negative (`negative_embed`) data where these opeartions fetch the corresponding embedding vectors for a set of given inputs. With that, we define negative sampling loss manually using the embeddings returned by the lookups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. Compute negative sampels for a given batch of data\n",
    "# Returns a [num_sampled] size Tensor\n",
    "negative_samples, _, _ = tf.nn.log_uniform_candidate_sampler(train_labels, num_true=1, num_sampled=num_sampled, \n",
    "                                                           unique=True, range_max=vocabulary_size)\n",
    "# 2. Look up embeddings for inputs, outputs and negative samples.\n",
    "in_embed = tf.nn.embedding_lookup(in_embeddings, train_dataset)\n",
    "out_embed = tf.nn.embedding_lookup(out_embeddings, tf.reshape(train_labels,[-1]))\n",
    "negative_embed = tf.nn.embedding_lookup(out_embeddings, negative_samples) \n",
    "\n",
    "# 3. Manually defining negative sample loss\n",
    "# As Tensorflow have a limited amount of flexibility in the built-in sampled_softmax_loss function,\n",
    "# we have to manually define the loss fuction.\n",
    "\n",
    "# 3.1. Computing the loss for the positive sample\n",
    "# Exactly we compute log(sigma(v_o * v_i^T)) with this equation\n",
    "loss = tf.reduce_mean(\n",
    "  tf.log(\n",
    "      tf.nn.sigmoid(\n",
    "          tf.reduce_sum(\n",
    "              tf.diag([1.0 for _ in range(batch_size)])*\n",
    "              tf.matmul(out_embed,tf.transpose(in_embed)),\n",
    "          axis=0)\n",
    "      )\n",
    "  )      \n",
    ")\n",
    "\n",
    "# 3.2. Computing loss for the negative samples\n",
    "# We compute sum(log(sigma(-v_no * v_i^T))) with the following\n",
    "# Note: The exact way this part is computed in TensorFlow library appears to be\n",
    "# by taking only the weights corresponding to true samples and negative samples\n",
    "# and then computing the softmax_cross_entropy_with_logits for that subset of weights.\n",
    "# More infor at: https://github.com/tensorflow/tensorflow/blob/r1.8/tensorflow/python/ops/nn_impl.py\n",
    "# Though the approach is different, the idea remains the same\n",
    "loss += tf.reduce_mean(\n",
    "  tf.reduce_sum(\n",
    "      tf.log(tf.nn.sigmoid(-tf.matmul(negative_embed,tf.transpose(in_embed)))),\n",
    "      axis=0\n",
    "  )\n",
    ")\n",
    "\n",
    "# The above is the log likelihood. \n",
    "# We would like to transform this to the negative log likelihood\n",
    "# to convert this to a loss. This provides us with\n",
    "# L = - (log(sigma(v_o * v_i^T))+sum(log(sigma(-v_no * v_i^T))))\n",
    "loss *= -1.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Word Similarities \n",
    "We calculate the similarity between two given words in terms of the cosine distance. To do this efficiently we use matrix operations to do so, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute the similarity between minibatch examples and all embeddings.\n",
    "# We use the cosine distance:\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square((in_embeddings+out_embeddings)/2.0), 1, keepdims=True))\n",
    "normalized_embeddings = out_embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(\n",
    "normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameter Optimizer\n",
    "\n",
    "We then define a constant learning rate and an optimizer which uses the Adagrad method. Feel free to experiment with other optimizers listed [here](https://www.tensorflow.org/api_guides/python/train)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Optimizer.\n",
    "optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the Original Skip-gram Algorithm\n",
    "\n",
    "Here we run the original skip-gram algorithm we defined above. Specifically, we first initialize variables, and then train the algorithm for many steps (`num_steps`). And every few steps we evaluate the algorithm on a fixed validation set and print out the words that appear to be closest for a given set of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2000: 23.100206\n",
      "Average loss at step 4000: 14.816036\n",
      "Average loss at step 6000: 12.212266\n",
      "Average loss at step 8000: 10.447992\n",
      "Average loss at step 10000: 9.534819\n",
      "Nearest to other: UNK, of, and, is, ., to, are, ,,\n",
      "Nearest to had: had, UNK, and, ., as, is, to, have,\n",
      "Nearest to are: of, UNK, and, ., is, to, ,, the,\n",
      "Nearest to also: UNK, of, ., is, to, and, as, have,\n",
      "Nearest to their: ., UNK, of, is, and, to, as, ,,\n",
      "Nearest to city: of, UNK, and, are, ., other, is, have,\n",
      "Nearest to at: of, ., UNK, and, is, ,, for, on,\n",
      "Nearest to 's: of, UNK, is, ., and, to, ), from,\n",
      "Nearest to one: of, UNK, ., and, is, in, to, for,\n",
      "Nearest to to: of, UNK, and, ., is, the, in, ,,\n",
      "Nearest to it: of, UNK, is, ., and, to, ), in,\n",
      "Nearest to he: UNK, of, ., and, is, on, have, (,\n",
      "Nearest to been: of, ., UNK, is, and, which, from, with,\n",
      "Nearest to on: of, UNK, and, ., is, ,, to, for,\n",
      "Nearest to has: of, UNK, is, and, ., ,, ), as,\n",
      "Nearest to the: UNK, ., the, and, to, ,, is, in,\n",
      "Average loss at step 12000: 8.726203\n",
      "Average loss at step 14000: 8.147189\n",
      "Average loss at step 16000: 7.557046\n",
      "Average loss at step 18000: 7.209419\n",
      "Average loss at step 20000: 7.071206\n",
      "Nearest to other: the, ., was, a, which, 's, of, that,\n",
      "Nearest to had: ., the, to, 's, by, was, a, of,\n",
      "Nearest to are: ., by, that, 's, the, and, to, ,,\n",
      "Nearest to also: a, ., by, was, which, the, to, UNK,\n",
      "Nearest to their: ., was, a, the, by, of, that, which,\n",
      "Nearest to city: ., the, to, was, which, a, and, of,\n",
      "Nearest to at: ., by, was, the, 's, which, to, and,\n",
      "Nearest to 's: ., the, was, by, of, a, to, that,\n",
      "Nearest to one: ., that, which, by, the, a, was, of,\n",
      "Nearest to to: ., the, by, that, was, a, of, ,,\n",
      "Nearest to it: ., by, was, that, the, a, to, which,\n",
      "Nearest to he: ., was, that, by, the, which, to, with,\n",
      "Nearest to been: a, 's, ., by, the, was, that, which,\n",
      "Nearest to on: ., the, to, was, that, a, ,, by,\n",
      "Nearest to has: ., a, by, the, which, of, that, to,\n",
      "Nearest to the: ., was, a, by, of, 's, to, that,\n",
      "Average loss at step 22000: 6.766422\n",
      "Average loss at step 24000: 6.839777\n",
      "Average loss at step 26000: 6.424795\n",
      "Average loss at step 28000: 6.410112\n",
      "Average loss at step 30000: 6.181797\n",
      "Nearest to other: UNK, for, is, ., ,, in, are, with,\n",
      "Nearest to had: is, UNK, ,, ., for, with, are, of,\n",
      "Nearest to are: is, UNK, for, ,, ., a, with, in,\n",
      "Nearest to also: also, UNK, are, for, ,, ., in, with,\n",
      "Nearest to their: UNK, for, ,, is, are, ., and, 's,\n",
      "Nearest to city: is, UNK, are, for, ,, ., of, which,\n",
      "Nearest to at: UNK, is, ., for, are, ,, with, of,\n",
      "Nearest to 's: UNK, is, ,, for, ., are, of, a,\n",
      "Nearest to one: UNK, is, for, are, ,, a, ., in,\n",
      "Nearest to to: is, ,, ., for, to, of, in, are,\n",
      "Nearest to it: UNK, is, ,, for, ., are, on, in,\n",
      "Nearest to he: is, UNK, ., ,, for, of, are, with,\n",
      "Nearest to been: UNK, been, for, are, ,, with, ., on,\n",
      "Nearest to on: on, UNK, for, ,, ., are, with, of,\n",
      "Nearest to has: UNK, has, for, are, ,, ., a, of,\n",
      "Nearest to the: is, ,, the, ., for, of, are, a,\n",
      "Average loss at step 32000: 6.143298\n",
      "Average loss at step 34000: 5.971997\n",
      "Average loss at step 36000: 5.854374\n",
      "Average loss at step 38000: 5.953786\n",
      "Average loss at step 40000: 5.756539\n",
      "Nearest to other: other, by, a, was, 's, on, he, of,\n",
      "Nearest to had: had, a, was, by, he, in, also, of,\n",
      "Nearest to are: and, are, by, was, from, ., in, of,\n",
      "Nearest to also: and, a, by, was, in, from, his, on,\n",
      "Nearest to their: and, by, was, a, he, 's, on, .,\n",
      "Nearest to city: and, he, was, by, 's, his, a, of,\n",
      "Nearest to at: and, was, a, by, 's, of, in, .,\n",
      "Nearest to 's: and, by, was, a, his, of, in, .,\n",
      "Nearest to one: and, was, by, a, in, ., his, of,\n",
      "Nearest to to: to, a, was, by, ., from, his, on,\n",
      "Nearest to it: and, was, by, a, from, his, ., ,,\n",
      "Nearest to he: and, a, by, was, his, on, from, 's,\n",
      "Nearest to been: and, a, by, his, was, in, on, from,\n",
      "Nearest to on: and, a, by, was, of, in, ., 's,\n",
      "Nearest to has: was, has, a, by, 's, ., on, of,\n",
      "Nearest to the: a, was, by, ., in, the, from, ,,\n",
      "Average loss at step 42000: 5.688243\n",
      "Average loss at step 44000: 5.680714\n",
      "Average loss at step 46000: 5.505318\n",
      "Average loss at step 48000: 5.375783\n",
      "Average loss at step 50000: 5.354121\n",
      "Nearest to other: other, ., and, of, the, in, for, its,\n",
      "Nearest to had: had, ., the, of, and, in, with, a,\n",
      "Nearest to are: are, ., the, and, of, for, a, by,\n",
      "Nearest to also: also, ., the, and, of, by, a, for,\n",
      "Nearest to their: ., the, and, their, of, for, a, in,\n",
      "Nearest to city: city, the, ., and, of, its, was, a,\n",
      "Nearest to at: ., the, and, by, of, at, 's, a,\n",
      "Nearest to 's: ., is, the, and, of, a, in, for,\n",
      "Nearest to one: the, ., one, and, of, a, in, 's,\n",
      "Nearest to to: ., the, and, to, of, a, for, in,\n",
      "Nearest to it: it, the, ., and, of, for, by, a,\n",
      "Nearest to he: and, ., he, by, the, of, a, which,\n",
      "Nearest to been: the, been, ., a, and, its, for, of,\n",
      "Nearest to on: ., the, and, on, of, in, a, 's,\n",
      "Nearest to has: has, ., the, of, and, a, for, in,\n",
      "Nearest to the: is, ., of, and, in, a, for, by,\n",
      "Average loss at step 52000: 5.267965\n",
      "Average loss at step 54000: 5.432471\n",
      "Average loss at step 56000: 5.441781\n",
      "Average loss at step 58000: 5.270313\n",
      "Average loss at step 60000: 5.313660\n",
      "Nearest to other: ., was, is, ,, that, on, of, with,\n",
      "Nearest to had: is, ., was, on, ,, with, but, from,\n",
      "Nearest to are: are, is, was, ,, that, on, an, a,\n",
      "Nearest to also: was, ., ,, is, an, that, but, a,\n",
      "Nearest to their: is, ., with, had, ,, was, of, 's,\n",
      "Nearest to city: city, ., was, ,, of, the, an, with,\n",
      "Nearest to at: ., is, was, ,, with, of, the, on,\n",
      "Nearest to 's: ., is, was, ,, on, with, from, of,\n",
      "Nearest to one: ., is, was, of, that, with, ,, on,\n",
      "Nearest to to: to, is, was, ,, with, that, a, of,\n",
      "Nearest to it: ., is, was, on, that, a, with, ,,\n",
      "Nearest to he: ., is, was, ,, were, 's, on, with,\n",
      "Nearest to been: ., been, at, ,, was, that, were, of,\n",
      "Nearest to on: ., is, was, ,, from, with, had, of,\n",
      "Nearest to has: is, has, ., ,, on, a, but, that,\n",
      "Nearest to the: was, is, the, ,, of, with, a, on,\n",
      "Average loss at step 62000: 5.185267\n",
      "Average loss at step 64000: 5.224732\n",
      "Average loss at step 66000: 5.094718\n",
      "Average loss at step 68000: 5.222364\n",
      "Average loss at step 70000: 5.100521\n",
      "Nearest to other: other, from, by, ., 's, were, in, of,\n",
      "Nearest to had: was, from, by, were, with, his, 's, in,\n",
      "Nearest to are: was, by, are, were, ., of, 's, in,\n",
      "Nearest to also: by, was, from, of, were, an, ., in,\n",
      "Nearest to their: was, by, from, were, ., 's, its, in,\n",
      "Nearest to city: was, by, from, of, ., in, 's, were,\n",
      "Nearest to at: by, at, from, of, ., 's, in, an,\n",
      "Nearest to 's: 's, by, from, of, ., with, were, in,\n",
      "Nearest to one: by, was, from, were, with, he, of, many,\n",
      "Nearest to to: by, from, ., to, were, of, 's, with,\n",
      "Nearest to it: was, it, from, were, ., an, his, of,\n",
      "Nearest to he: by, was, of, were, from, with, his, its,\n",
      "Nearest to been: been, by, from, of, ., in, his, were,\n",
      "Nearest to on: was, by, ., of, in, from, 's, with,\n",
      "Nearest to has: by, has, from, were, 's, ., of, with,\n",
      "Nearest to the: from, was, ., of, in, 's, and, an,\n",
      "Average loss at step 72000: 5.171802\n",
      "Average loss at step 74000: 5.243292\n",
      "Average loss at step 76000: 5.096733\n",
      "Average loss at step 78000: 5.124353\n",
      "Average loss at step 80000: 5.037227\n",
      "Nearest to other: from, is, for, ., by, in, and, were,\n",
      "Nearest to had: had, and, from, in, ., for, was, were,\n",
      "Nearest to are: is, by, from, in, ., for, were, and,\n",
      "Nearest to also: from, is, for, and, by, in, ., its,\n",
      "Nearest to their: by, is, from, in, were, ., and, for,\n",
      "Nearest to city: is, from, by, in, of, and, for, were,\n",
      "Nearest to at: from, in, ., by, for, and, of, were,\n",
      "Nearest to 's: from, in, by, ., and, for, is, were,\n",
      "Nearest to one: from, is, by, for, in, ., were, on,\n",
      "Nearest to to: to, is, by, for, in, ., and, were,\n",
      "Nearest to it: it, for, by, is, ., in, and, were,\n",
      "Nearest to he: from, is, by, in, and, for, ., of,\n",
      "Nearest to been: by, been, for, from, ., in, and, are,\n",
      "Nearest to on: is, from, by, in, ., and, for, of,\n",
      "Nearest to has: is, in, for, by, ., from, and, were,\n",
      "Nearest to the: the, by, in, ., is, for, and, of,\n",
      "Average loss at step 82000: 5.147896\n",
      "Average loss at step 84000: 5.037831\n",
      "Average loss at step 86000: 4.963351\n",
      "Average loss at step 88000: 4.999875\n",
      "Average loss at step 90000: 5.001885\n",
      "Nearest to other: a, which, by, were, other, their, was, its,\n",
      "Nearest to had: by, which, a, was, had, were, but, its,\n",
      "Nearest to are: are, a, by, which, were, was, their, is,\n",
      "Nearest to also: also, a, by, was, which, 's, its, were,\n",
      "Nearest to their: their, a, was, were, which, by, its, is,\n",
      "Nearest to city: city, which, is, was, their, its, a, 's,\n",
      "Nearest to at: at, a, by, which, was, were, their, its,\n",
      "Nearest to 's: 's, a, by, which, was, is, their, its,\n",
      "Nearest to one: which, one, a, was, by, its, were, at,\n",
      "Nearest to to: to, a, was, which, by, were, their, from,\n",
      "Nearest to it: was, a, its, were, by, it, their, which,\n",
      "Nearest to he: was, a, which, he, their, its, were, by,\n",
      "Nearest to been: was, a, which, by, been, were, is, its,\n",
      "Nearest to on: on, was, a, by, 's, which, from, is,\n",
      "Nearest to has: a, was, by, which, were, has, their, is,\n",
      "Nearest to the: a, was, by, the, 's, which, from, its,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 92000: 4.976384\n",
      "Average loss at step 94000: 4.923462\n",
      "Average loss at step 96000: 4.948530\n",
      "Average loss at step 98000: 4.889717\n",
      "Average loss at step 100000: 4.825054\n",
      "Nearest to other: is, with, other, in, for, from, which, and,\n",
      "Nearest to had: with, for, ., had, in, by, which, from,\n",
      "Nearest to are: are, ., with, for, which, in, while, by,\n",
      "Nearest to also: also, ., with, in, for, which, from, UNK,\n",
      "Nearest to their: ., is, with, for, and, in, from, which,\n",
      "Nearest to city: city, in, ., with, its, for, of, from,\n",
      "Nearest to at: ., at, is, in, for, from, which, ,,\n",
      "Nearest to 's: 's, with, is, in, for, from, and, its,\n",
      "Nearest to one: ., one, with, in, which, its, for, from,\n",
      "Nearest to to: ., is, for, in, to, which, from, and,\n",
      "Nearest to it: it, ., with, in, for, which, from, its,\n",
      "Nearest to he: ., from, with, for, in, by, an, which,\n",
      "Nearest to been: ., been, for, which, with, in, from, a,\n",
      "Nearest to on: in, with, from, is, for, by, ,, on,\n",
      "Nearest to has: ., has, for, with, in, its, which, from,\n",
      "Nearest to the: is, the, in, with, for, from, its, which,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "skip_gram_loss_original = [] # Collect the sequential loss values for plotting purposes\n",
    "\n",
    "# ConfigProto is a way of providing various configuration settings \n",
    "# required to execute the graph\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as session:\n",
    "    \n",
    "  # Initialize the variables in the graph\n",
    "  tf.global_variables_initializer().run()\n",
    "  print('Initialized')\n",
    "  average_loss = 0\n",
    "    \n",
    "  # Train the Word2vec model for num_step iterations\n",
    "  for step in range(num_steps):\n",
    "        \n",
    "    # Generate a single batch of data\n",
    "    batch_data, batch_labels = generate_batch_skip_gram(\n",
    "      batch_size, window_size)\n",
    "    \n",
    "    # Populate the feed_dict and run the optimizer (minimize loss)\n",
    "    # and compute the loss\n",
    "    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "    _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    \n",
    "    # Update the average loss variable\n",
    "    average_loss += l\n",
    "\n",
    "    if (step+1) % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss = average_loss / 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step %d: %f' % (step+1, average_loss))\n",
    "      skip_gram_loss_original.append(average_loss)\n",
    "      average_loss = 0\n",
    "    \n",
    "    # Here we compute the top_k closest words for a given validation word\n",
    "    # in terms of the cosine distance\n",
    "    # We do this for all the words in the validation set\n",
    "    # Note: This is an expensive step\n",
    "    if (step+1) % 10000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in range(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8 # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "        log = 'Nearest to %s:' % valid_word\n",
    "        for k in range(top_k):\n",
    "          close_word = reverse_dictionary[nearest[k]]\n",
    "          log = '%s %s,' % (log, close_word)\n",
    "        print(log)\n",
    "        \n",
    "  skip_gram_original_final_embeddings = normalized_embeddings.eval()\n",
    "np.save('skip_original_embeddings',skip_gram_original_final_embeddings)\n",
    "\n",
    "with open('skip_original_losses.csv', 'wt') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    writer.writerow(skip_gram_loss_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Skip Gram Loss vs Original Skip Gram Loss\n",
    "Here we plot skip gram loss we got from chapter 3 with the original skip gram loss we just ran to see which one performs better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SetxN+IpQfRpj3ZMKZT3ZmM5kX+ocH56Hfr0BbsLFa+GSTfw2uErDVVxP0kA+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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ca1ddd240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the skip-gram losses from the calculations we did in Chapter 3\n",
    "# So you need to make sure you have this csv file before running the code below\n",
    "skip_loss_path = os.path.join('..','ch3','skip_losses.csv')\n",
    "with open(skip_loss_path, 'rt') as f:\n",
    "    reader = csv.reader(f,delimiter=',')\n",
    "    for r_i,row in enumerate(reader):\n",
    "        if r_i == 0:\n",
    "            skip_gram_loss =  [float(s) for s in row]\n",
    "\n",
    "\n",
    "pylab.figure(figsize=(15,5))  # figure in inches\n",
    "\n",
    "# Define the x axis\n",
    "x = np.arange(len(skip_gram_loss))*2000\n",
    "  \n",
    "# Plot the skip_gram_loss (loaded from chapter 3)\n",
    "pylab.plot(x, skip_gram_loss, label=\"Skip-Gram (Improved)\",linestyle='--',linewidth=2)    \n",
    "# Plot the original skip gram loss from what we just ran\n",
    "pylab.plot(x, skip_gram_loss_original, label=\"Skip-Gram (Original)\",linewidth=2)\n",
    "\n",
    "# Set some text around the plot\n",
    "pylab.title('Original vs Improved Skip-Gram Loss Decrease Over Time',fontsize=24)\n",
    "pylab.xlabel('Iterations',fontsize=22)\n",
    "pylab.ylabel('Loss',fontsize=22)\n",
    "pylab.legend(loc=1,fontsize=22)\n",
    "\n",
    "# use for saving the figure if needed\n",
    "pylab.savefig('loss_skipgram_original_vs_impr.jpg')\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Skip-Gram Loss vs CBOW Loss\n",
    "\n",
    "Here we compare the skip-gram loss and CBOW loss to compare which loss decreases quicker. Refer the text for an analysis of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uXbrfGJNYXbqTMmBMS0tjyZIloc6GUkoppZRSSoWEiGypPpW2YVRKKaWUUkop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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ca1ddd208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the skip-gram losses from the calculations we did in Chapter 3\n",
    "# So you need to make sure you have this csv file before running the code below\n",
    "cbow_loss_path = os.path.join('..','ch3','cbow_losses.csv')\n",
    "with open(cbow_loss_path, 'rt') as f:\n",
    "    reader = csv.reader(f,delimiter=',')\n",
    "    for r_i,row in enumerate(reader):\n",
    "        if r_i == 0:\n",
    "            cbow_loss =  [float(s) for s in row]\n",
    "\n",
    "pylab.figure(figsize=(15,5))  # in inches\n",
    "\n",
    "# Define the x axis\n",
    "x = np.arange(len(skip_gram_loss))*2000\n",
    "\n",
    "# Plot the skip_gram_loss (loaded from chapter 3)\n",
    "pylab.plot(x, skip_gram_loss, label=\"Skip-Gram\",linestyle='--',linewidth=2)    \n",
    "# Plot the cbow_loss (loaded from chapter 3)\n",
    "pylab.plot(x, cbow_loss, label=\"CBOW\",linewidth=2)\n",
    "\n",
    "# Set some text around the plot\n",
    "pylab.title('Skip-Gram vs CBOW Loss Decrease Over Time',fontsize=24)\n",
    "pylab.xlabel('Iterations',fontsize=22)\n",
    "pylab.ylabel('Loss',fontsize=22)\n",
    "pylab.legend(loc=1,fontsize=22)\n",
    "\n",
    "# use for saving the figure if needed\n",
    "pylab.savefig('loss_skipgram_vs_cbow.png')\n",
    "pylab.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting TSNE Embeddings for Skip-Gram and CBOW Side by Side\n",
    "\n",
    "Loss itself is not an adequate measure of performance. Therefore we visualize the learned embeddings by projecting the embeddings to a two dimensional canvas with a technique known as t-SNE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_clustered_embeddings(embeddings,distance_threshold,sample_threshold):\n",
    "    ''' \n",
    "    Find only the closely clustered embeddings. \n",
    "    This gets rid of more sparsly distributed word embeddings and make the visualization clearer\n",
    "    This is useful for t-SNE visualization\n",
    "    \n",
    "    distance_threshold: maximum distance between two points to qualify as neighbors\n",
    "    sample_threshold: number of neighbors required to be considered a cluster\n",
    "    '''\n",
    "    \n",
    "    # calculate cosine similarity\n",
    "    cosine_sim = np.dot(embeddings,np.transpose(embeddings))\n",
    "    norm = np.dot(np.sum(embeddings**2,axis=1).reshape(-1,1),np.sum(np.transpose(embeddings)**2,axis=0).reshape(1,-1))\n",
    "    assert cosine_sim.shape == norm.shape\n",
    "    cosine_sim /= norm\n",
    "    \n",
    "    # make all the diagonal entries zero otherwise this will be picked as highest\n",
    "    np.fill_diagonal(cosine_sim, -1.0)\n",
    "    \n",
    "    argmax_cos_sim = np.argmax(cosine_sim, axis=1)\n",
    "    mod_cos_sim = cosine_sim\n",
    "    # find the maximums in a loop to count if there are more than n items above threshold\n",
    "    for _ in range(sample_threshold-1):\n",
    "        argmax_cos_sim = np.argmax(cosine_sim, axis=1)\n",
    "        mod_cos_sim[np.arange(mod_cos_sim.shape[0]),argmax_cos_sim] = -1\n",
    "    \n",
    "    max_cosine_sim = np.max(mod_cos_sim,axis=1)\n",
    "\n",
    "    return np.where(max_cosine_sim>distance_threshold)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting the skip-gram and CBOW embeddings to a t-SNE \n",
    "We fit skip-gram and CBOW embeddings to a t-SNE to get their mapping on a two dimensional surface. We only visualize densely clustered data points to avoid clutter in the visualization. This is achieved with the above function `find_clustered_embeddings`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting embeddings to T-SNE (skip-gram and CBOW)\n",
      "\tSkip-gram\n",
      "\tCBOW\n",
      "Pruning the T-SNE embeddings (skip-gram and CBOW)\n",
      "\tSkip-gram\n",
      "\tCBOW\n",
      "Out of  1000  samples (skip-gram),  123  samples were selected by pruning\n",
      "Out of  1000  samples (CBOW),  123  samples were selected by pruning\n"
     ]
    }
   ],
   "source": [
    "# Load the previously saved embeddings from Chapter 3 exercise\n",
    "skip_emb_path = os.path.join('..','ch3','skip_embeddings.npy')\n",
    "cbow_emb_path = os.path.join('..','ch3','cbow_embeddings.npy')\n",
    "\n",
    "skip_gram_final_embeddings = np.load(skip_emb_path)\n",
    "cbow_final_embeddings = np.load(cbow_emb_path)\n",
    "\n",
    "num_points = 1000 # we will use a large sample space to build the T-SNE manifold and then prune it using cosine similarity\n",
    "\n",
    "# Create a t-SNE object from scikit-learn\n",
    "tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
    "\n",
    "print('Fitting embeddings to T-SNE (skip-gram and CBOW)')\n",
    "# Get the T-SNE manifold for skip-gram embeddings\n",
    "print('\\tSkip-gram')\n",
    "sg_selected_embeddings = skip_gram_final_embeddings[:num_points, :]\n",
    "sg_two_d_embeddings = tsne.fit_transform(sg_selected_embeddings)\n",
    "\n",
    "# Get the T-SNE manifold for CBOW embeddings\n",
    "print('\\tCBOW')\n",
    "cbow_selected_embeddings = cbow_final_embeddings[:num_points, :]\n",
    "cbow_two_d_embeddings = tsne.fit_transform(cbow_selected_embeddings)\n",
    "\n",
    "print('Pruning the T-SNE embeddings (skip-gram and CBOW)')\n",
    "# Prune the embeddings by getting ones only more than n-many sample above the similarity threshold\n",
    "# this unclutters the visualization\n",
    "# Prune skip-gram\n",
    "print('\\tSkip-gram')\n",
    "sg_selected_ids = find_clustered_embeddings(sg_selected_embeddings,.3,10)\n",
    "sg_two_d_embeddings = sg_two_d_embeddings[sg_selected_ids,:]\n",
    "# Prune CBOW\n",
    "print('\\tCBOW')\n",
    "cbow_selected_ids = find_clustered_embeddings(cbow_selected_embeddings,.3,10)\n",
    "cbow_two_d_embeddings = cbow_two_d_embeddings[cbow_selected_ids,:]\n",
    "\n",
    "# Some stats about pruning\n",
    "print('Out of ',num_points,' samples (skip-gram), ', sg_selected_ids.shape[0],' samples were selected by pruning')\n",
    "print('Out of ',num_points,' samples (CBOW), ', cbow_selected_ids.shape[0],' samples were selected by pruning')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the Embeddings\n",
    "Here we plot the embeddings side by side, each embedding layer on its own subplot. We also use different colors for data points to improve clarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Define Label colors for %d 20\n",
      "Running K-Means for skip-gram\n",
      "Running K-Means for CBOW\n",
      "K-Means ran successfully\n",
      "Plotting results\n"
     ]
    },
    {
     "data": {
      "image/png": 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nnO9/jTHmWqCG63BOqbRFpIRRQEpEROQKYq2NzS1dRQHrOQPMcn0MJsOMmPOYBsTj5Bv/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i/f7du3eeeddyhfvjwajYYuXboU+nI/JCSEevXqYWFhgZ2dHUOHDiU1NbXAdfbv35+yZctibW3N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NECFxcXvL296d+/P0eOHLHqYxgGsbGxVm1paWkYhmHZI6LQ5MmTMZlMuLi40LZtWzZv3ozJ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\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9+/Y7jm1tbc1LL73EsGHDOHv2bLnJK1HNqO6wQu1OcSGEEBX2dP72SgghhHhKaXrYG+wh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\nOde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uLo63336bywcPcjkri8kmJnT6096yxhUqsLtPHyZNmsS9e/coWbKk7rXw8HAGDx7M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\nXi+s5s2bs3nzZhYuXEj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ca2327390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_embeddings_side_by_side(sg_embeddings, cbow_embeddings, sg_labels, cbow_labels):\n",
    "  ''' Plots word embeddings of skip-gram and CBOW side by side as subplots\n",
    "  '''\n",
    "  # number of clusters for each word embedding \n",
    "  # clustering is used to assign different colors as a visual aid\n",
    "  n_clusters = 20 \n",
    "    \n",
    "  # automatically build a discrete set of colors, each for cluster\n",
    "  print('Define Label colors for %d',n_clusters)\n",
    "  label_colors = [pylab.cm.spectral(float(i) /n_clusters) for i in range(n_clusters)]\n",
    "  \n",
    "  # Make sure number of embeddings and their labels are the same\n",
    "  assert sg_embeddings.shape[0] >= len(sg_labels), 'More labels than embeddings'\n",
    "  assert cbow_embeddings.shape[0] >= len(cbow_labels), 'More labels than embeddings'\n",
    "  \n",
    "  print('Running K-Means for skip-gram')\n",
    "  # Define K-Means\n",
    "  sg_kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=0).fit(sg_embeddings)\n",
    "  sg_kmeans_labels = sg_kmeans.labels_\n",
    "  sg_cluster_centroids = sg_kmeans.cluster_centers_\n",
    "\n",
    "  print('Running K-Means for CBOW')\n",
    "  cbow_kmeans = KMeans(n_clusters=n_clusters, init='k-means++', random_state=0).fit(cbow_embeddings)\n",
    "  cbow_kmeans_labels = cbow_kmeans.labels_\n",
    "  cbow_cluster_centroids = cbow_kmeans.cluster_centers_\n",
    "    \n",
    "  print('K-Means ran successfully')\n",
    "\n",
    "  print('Plotting results')\n",
    "  pylab.figure(figsize=(25,20))  # in inches\n",
    "\n",
    "  # Get the first subplot\n",
    "  pylab.subplot(1, 2, 1) \n",
    "\n",
    "  # Plot all the embeddings and their corresponding words for skip-gram\n",
    "  for i, (label,klabel) in enumerate(zip(sg_labels,sg_kmeans_labels)):\n",
    "    center = sg_cluster_centroids[klabel,:]\n",
    "    x, y = cbow_embeddings[i,:]\n",
    "    \n",
    "    # This is just to spread the data points around a bit\n",
    "    # So that the labels are clearer\n",
    "    # We repel datapoints from the cluster centroid\n",
    "    if x < center[0]:\n",
    "        x += -abs(np.random.normal(scale=2.0))\n",
    "    else:\n",
    "        x += abs(np.random.normal(scale=2.0))\n",
    "        \n",
    "    if y < center[1]:\n",
    "        y += -abs(np.random.normal(scale=2.0))\n",
    "    else:\n",
    "        y += abs(np.random.normal(scale=2.0))\n",
    "        \n",
    "    pylab.scatter(x, y, c=label_colors[klabel])    \n",
    "    x = x if np.random.random()<0.5 else x + 10\n",
    "    y = y if np.random.random()<0.5 else y - 10\n",
    "    pylab.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points',\n",
    "                   ha='right', va='bottom',fontsize=16)\n",
    "  pylab.title('t-SNE for Skip-Gram',fontsize=24)\n",
    "\n",
    "  # Get the second subplot\n",
    "  pylab.subplot(1, 2, 2)  \n",
    "\n",
    "  # Plot all the embeddings and their corresponding words for CBOW\n",
    "  for i, (label,klabel) in enumerate(zip(cbow_labels,cbow_kmeans_labels)):\n",
    "    center = cbow_cluster_centroids[klabel,:]\n",
    "    x, y = cbow_embeddings[i,:]  \n",
    "    \n",
    "    # This is just to spread the data points around a bit\n",
    "    # So that the labels are clearer\n",
    "    # We repel datapoints from the cluster centroid\n",
    "    if x < center[0]:\n",
    "        x += -abs(np.random.normal(scale=2.0))\n",
    "    else:\n",
    "        x += abs(np.random.normal(scale=2.0))\n",
    "        \n",
    "    if y < center[1]:\n",
    "        y += -abs(np.random.normal(scale=2.0))\n",
    "    else:\n",
    "        y += abs(np.random.normal(scale=2.0))\n",
    "        \n",
    "    pylab.scatter(x, y, c=label_colors[klabel])  \n",
    "    x = x if np.random.random()<0.5 else x + np.random.randint(0,10)\n",
    "    y = y + np.random.randint(0,5) if np.random.random()<0.5 else y - np.random.randint(0,5)\n",
    "    pylab.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points',\n",
    "                   ha='right', va='bottom',fontsize=16)\n",
    "\n",
    "  pylab.title('t-SNE for CBOW',fontsize=24)\n",
    "  # use for saving the figure if needed\n",
    "  pylab.savefig('tsne_skip_vs_cbow.png')\n",
    "  pylab.show()\n",
    "\n",
    "# Run the function\n",
    "sg_words = [reverse_dictionary[i] for i in sg_selected_ids]\n",
    "cbow_words = [reverse_dictionary[i] for i in cbow_selected_ids]\n",
    "plot_embeddings_side_by_side(sg_two_d_embeddings, cbow_two_d_embeddings, sg_words,cbow_words)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBOW Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the data generation process\n",
    "We need to define a new data generator for CBOW. Shape of the new input array is (batch_size, context_window*2). That is, a batch in CBOW captures all the words in the context of a given word."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "with window_size = 1:\n",
      "    batch: [['propaganda', 'a'], ['is', 'concerted'], ['a', 'set'], ['concerted', 'of'], ['set', 'messages'], ['of', 'aimed'], ['messages', 'at'], ['aimed', 'influencing']]\n",
      "    labels: ['is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at']\n",
      "\n",
      "with window_size = 2:\n",
      "    batch: [['propaganda', 'is', 'concerted', 'set'], ['is', 'a', 'set', 'of'], ['a', 'concerted', 'of', 'messages'], ['concerted', 'set', 'messages', 'aimed'], ['set', 'of', 'aimed', 'at'], ['of', 'messages', 'at', 'influencing'], ['messages', 'aimed', 'influencing', 'the'], ['aimed', 'at', 'the', 'opinions']]\n",
      "    labels: ['a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing']\n"
     ]
    }
   ],
   "source": [
    "data_index = 0\n",
    "\n",
    "def generate_batch_cbow(batch_size, window_size):\n",
    "    # window_size is the amount of words we're looking at from each side of a given word\n",
    "    # creates a single batch\n",
    "    \n",
    "    # data_index is updated by 1 everytime we read a set of data point\n",
    "    global data_index\n",
    "\n",
    "    # span defines the total window size, where\n",
    "    # data we consider at an instance looks as follows. \n",
    "    # [ skip_window target skip_window ]\n",
    "    # e.g if skip_window = 2 then span = 5\n",
    "    span = 2 * window_size + 1 # [ skip_window target skip_window ]\n",
    "\n",
    "    # two numpy arras to hold target words (batch)\n",
    "    # and context words (labels)\n",
    "    # Note that batch has span-1=2*window_size columns\n",
    "    batch = np.ndarray(shape=(batch_size,span-1), dtype=np.int32)\n",
    "    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "    \n",
    "    # The buffer holds the data contained within the span\n",
    "    buffer = collections.deque(maxlen=span)\n",
    "\n",
    "    # Fill the buffer and update the data_index\n",
    "    for _ in range(span):\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "\n",
    "    # Here we do the batch reading\n",
    "    # We iterate through each batch index\n",
    "    # For each batch index, we iterate through span elements\n",
    "    # to fill in the columns of batch array\n",
    "    for i in range(batch_size):\n",
    "        target = window_size  # target label at the center of the buffer\n",
    "        target_to_avoid = [ window_size ] # we only need to know the words around a given word, not the word itself\n",
    "\n",
    "        # add selected target to avoid_list for next time\n",
    "        col_idx = 0\n",
    "        for j in range(span):\n",
    "            # ignore the target word when creating the batch\n",
    "            if j==span//2:\n",
    "                continue\n",
    "            batch[i,col_idx] = buffer[j] \n",
    "            col_idx += 1\n",
    "        labels[i, 0] = buffer[target]\n",
    "\n",
    "        # Everytime we read a data point,\n",
    "        # we need to move the span by 1\n",
    "        # to create a fresh new span\n",
    "        buffer.append(data[data_index])\n",
    "        data_index = (data_index + 1) % len(data)\n",
    "\n",
    "    return batch, labels\n",
    "\n",
    "for window_size in [1,2]:\n",
    "    data_index = 0\n",
    "    batch, labels = generate_batch_cbow(batch_size=8, window_size=window_size)\n",
    "    print('\\nwith window_size = %d:' % (window_size))\n",
    "    print('    batch:', [[reverse_dictionary[bii] for bii in bi] for bi in batch])\n",
    "    print('    labels:', [reverse_dictionary[li] for li in labels.reshape(8)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Using Candidate Sampling with the Unigram Distribution for Negative Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unigram distribution\n",
    "The unigram distribution $U(w_i)$ for a given word $w_i$ is given by,\n",
    "\n",
    "$ P(w_i|w_1,...,w_{i-1}) \\simeq P(w_i) = \\frac{count(w_i)}{\\sum_{w_j \\in corpus}count(w_j)} = U(w_i) $\n",
    "\n",
    "The original paper found that if words are sampled the noise for the negative sampling with a particular distribution gives the best results. And the distribution is given by,\n",
    "\n",
    "$ U(w)^{3/4} / Z $ where $Z$ is a constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First 10 Unigram probabilities\n",
      "[0.020597949103141815, 0.06751836004435337, 0.0547611125957731, 0.03599217447562499, 0.03461699391063737, 0.026378111863097367, 0.02315338634866199, 0.019532861190981958, 0.01753065066485412, 0.009183146910709386]\n"
     ]
    }
   ],
   "source": [
    "# Creating vocabulary file and unigram counts\n",
    "# Requried by the tf.nn.fixed_unigram_candidate_sampler\n",
    "\n",
    "# vocabulary file: Each valid line in this file (which should have a CSV-like format) corresponds to a valid word ID. IDs are in sequential order.\n",
    "# unigrams: A list of unigram counts or probabilities, one per ID in sequential order. Exactly one of vocab_file and unigrams should be passed to this operation.\n",
    "\n",
    "word_count_dictionary = {}\n",
    "unigrams = [0 for _ in range(vocabulary_size)]\n",
    "for word,w_count in count:\n",
    "    w_idx = dictionary[word]\n",
    "    unigrams[w_idx] = w_count*1.0/token_count\n",
    "    word_count_dictionary[w_idx] = w_count\n",
    "print('First 10 Unigram probabilities')\n",
    "print(unigrams[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Hyperparameters\n",
    "\n",
    "Here we define several hyperparameters including `batch_size` (amount of samples in a single batch) `embedding_size` (size of embedding vectors) `window_size` (context window size)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 128 # Data points in a single batch\n",
    "embedding_size = 128 # Dimension of the embedding vector.\n",
    "# How many words to consider left and right.\n",
    "# Skip gram by design does not require to have all the context words in a given step\n",
    "# However, for CBOW that's a requirement, so we limit the window size\n",
    "window_size = 2 \n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors\n",
    "valid_size = 16 # Random set of words to evaluate similarity on.\n",
    "# We sample valid datapoints randomly from a large window without always being deterministic\n",
    "valid_window = 50\n",
    "\n",
    "# When selecting valid examples, we select some of the most frequent words as well as\n",
    "# some moderately rare words as well\n",
    "valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
    "valid_examples = np.append(valid_examples,random.sample(range(1000, 1000+valid_window), valid_size),axis=0)\n",
    "\n",
    "num_sampled = 32 # Number of negative examples to sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Inputs and Outputs\n",
    "\n",
    "Here we define placeholders for feeding in training inputs and outputs (each of size `batch_size`) and a constant tensor to contain validation examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "# Training input data (target word IDs). Note that it has 2*window_size columns\n",
    "train_dataset = tf.placeholder(tf.int32, shape=[batch_size,2*window_size])\n",
    "# Training input label data (context word IDs)\n",
    "train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "# Validation input data, we don't need a placeholder\n",
    "# as we have already defined the IDs of the words selected\n",
    "# as validation data\n",
    "valid_dataset = tf.constant(valid_examples, dtype=tf.int32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining Model Parameters and Other Variables\n",
    "We now define several TensorFlow variables such as an embedding layer (`embeddings`) and neural network parameters (`softmax_weights` and `softmax_biases`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Variables.\n",
    "\n",
    "# Embedding layer, contains the word embeddings\n",
    "embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0,dtype=tf.float32))\n",
    "\n",
    "# Softmax Weights and Biases\n",
    "softmax_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                 stddev=0.5 / math.sqrt(embedding_size),dtype=tf.float32))\n",
    "softmax_biases = tf.Variable(tf.random_uniform([vocabulary_size],0.0,0.01))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Model Computations\n",
    "\n",
    "We first defing a lookup function to fetch the corresponding embedding vectors for a set of given inputs. Concretely, we define $2\\times$`window_size` embedding lookups. We then concatenate all these looked up embedding vectors to form a matrix of size `[batch_size, embedding_size, 2*window_size]`. Thereafter, we average these embedding lookups to produce an average embeddings of size `[batch_size, embedding_size]`. With that, we define negative sampling loss function `tf.nn.sampled_softmax_loss` which takes in the embedding vectors and previously defined neural network parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defining 4 embedding lookups representing each word in the context\n",
      "Stacked embedding size: [128, 128, 4]\n",
      "Reduced mean embedding size: [128, 128]\n",
      "WARNING:tensorflow:From c:\\users\\thushan\\documents\\python_virtualenvs\\tensorflow_venv\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py:1344: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Model.\n",
    "# Look up embeddings for a batch of inputs.\n",
    "# Here we do embedding lookups for each column in the input placeholder\n",
    "# and then average them to produce an embedding_size word vector\n",
    "stacked_embedings = None\n",
    "print('Defining %d embedding lookups representing each word in the context'%(2*window_size))\n",
    "for i in range(2*window_size):\n",
    "    embedding_i = tf.nn.embedding_lookup(embeddings, train_dataset[:,i])        \n",
    "    x_size,y_size = embedding_i.get_shape().as_list()\n",
    "    if stacked_embedings is None:\n",
    "        stacked_embedings = tf.reshape(embedding_i,[x_size,y_size,1])\n",
    "    else:\n",
    "        stacked_embedings = tf.concat(axis=2,values=[stacked_embedings,tf.reshape(embedding_i,[x_size,y_size,1])])\n",
    "\n",
    "assert stacked_embedings.get_shape().as_list()[2]==2*window_size\n",
    "print(\"Stacked embedding size: %s\"%stacked_embedings.get_shape().as_list())\n",
    "mean_embeddings =  tf.reduce_mean(stacked_embedings,2,keepdims=False)\n",
    "print(\"Reduced mean embedding size: %s\"%mean_embeddings.get_shape().as_list())\n",
    "\n",
    "# Compute the softmax loss, using a sample of the negative labels each time.\n",
    "# inputs are embeddings of the train words\n",
    "# with this loss we optimize weights, biases, embeddings\n",
    "\n",
    "# However, unlike at the previous instance (Chapter 3) we use a different sample to sampel negative classes\n",
    "# Particularly we use a unigram candidate sampler, to which we provide\n",
    "# the unigram probabilities we computed earlier. For details about the passed arguments\n",
    "# Refer the text in Chapter 4\n",
    "candidate_sampler = tf.nn.fixed_unigram_candidate_sampler(true_classes = tf.cast(train_labels,dtype=tf.int64), num_true = 1, \n",
    "                                      num_sampled = num_sampled, \n",
    "                                      unique = True, range_max = vocabulary_size, \n",
    "                                      distortion=0.75, \n",
    "                                      num_reserved_ids=0, \n",
    "                                      unigrams=unigrams, name='unigram_sampler')\n",
    "\n",
    "# The loss is very similar to what we defined in Chapter 3, except for\n",
    "# passing the above defined sampler to the function.\n",
    "loss = tf.reduce_mean(\n",
    "    tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=mean_embeddings,\n",
    "                           labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size,\n",
    "                              sampled_values=candidate_sampler))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameter Optimizer\n",
    "\n",
    "We then define a learning rate as a constant and an optimizer which uses the Adagrad method. Feel free to experiment with other optimizers listed [here](https://www.tensorflow.org/api_guides/python/train)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Word Similarities \n",
    "We calculate the similarity between two given words in terms of the cosine distance. To do this efficiently we use matrix operations to do so, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Compute the similarity between minibatch examples and all embeddings.\n",
    "# We use the cosine distance:\n",
    "norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))\n",
    "normalized_embeddings = embeddings / norm\n",
    "valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)\n",
    "similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running CBOW with Unigram Candidate Sampling\n",
    "\n",
    "Here we run the CBOW algorithm with unigram based candidate sampling, we defined above. Specifically, we first initialize variables, and then train the algorithm for many steps (`num_steps`). And every few steps we evaluate the algorithm on a fixed validation set and print out the words that appear to be closest for a given set of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2000: 3.432433\n",
      "Average loss at step 4000: 2.900289\n",
      "Average loss at step 6000: 2.774288\n",
      "Average loss at step 8000: 2.691287\n",
      "Average loss at step 10000: 2.629643\n",
      "Nearest to have: had, has, challenges, be, designate, albisaurus, magister, depose,\n",
      "Nearest to other: year., tian, rough, striped, gebhart, edgeworth, antisubmarine, kilogram,\n",
      "Nearest to had: has, have, would, was, blisters, duma, sweetener, pertains,\n",
      "Nearest to 's: voivode, toucouleurs, glorified, zero-fare, his, osmotic, dirac, oligodendrocytes,\n",
      "Nearest to their: its, his, the, outweighed, overruns, jinjur, gsm, enough,\n",
      "Nearest to were: are, was, unethical, pressurised, documenting, cheng, stalks, becomes,\n",
      "Nearest to on: knowable, feynman, herrn, recombination, virions, monologues, fuerza, pope,\n",
      "Nearest to is: was, are, has, 19°, intermarriages, slovensko, cuticle, esoteric,\n",
      "Nearest to ,: ;, ., yudenich, describing, forelimb, well-represented, 20,700, ''paradise,\n",
      "Nearest to which: that, who, but, still, greater, phonetics, −π, routine,\n",
      "Nearest to its: the, their, his, alexei, bharat, madhhabs, methodological, cherokee,\n",
      "Nearest to the: its, a, this, any, an, their, each, rock-mass,\n",
      "Nearest to or: and, like, tripura, robe, idhari, two-front, mummies, tyrannosaurid,\n",
      "Nearest to he: it, they, s-layers, chaperones, o'dea, olbers, tiber, overhauling,\n",
      "Nearest to city: persistence, reversed, nuys, emphasises, grease, infidels, pratica, corporate,\n",
      "Nearest to (: per, parenthood, montt, malibu, 1640., signify, magill, christchurch,\n",
      "Average loss at step 12000: 2.599801\n",
      "Average loss at step 14000: 2.552338\n",
      "Average loss at step 16000: 2.520618\n",
      "Average loss at step 18000: 2.510831\n",
      "Average loss at step 20000: 2.490122\n",
      "Nearest to have: had, has, challenges, be, designate, albisaurus, limestones, jeremiah,\n",
      "Nearest to other: gebhart, year., striped, edgeworth, tian, technologies, many, antisubmarine,\n",
      "Nearest to had: has, have, would, was, were, duma, sweetener, omega-3,\n",
      "Nearest to 's: voivode, glorified, toucouleurs, his, her, inverness, oligodendrocytes, gare,\n",
      "Nearest to their: its, his, her, iudeu, these, overruns, the, theodosius,\n",
      "Nearest to were: are, was, grows, had, be, stalks, becomes, pressurised,\n",
      "Nearest to on: feynman, knowable, virions, cpgb, herrn, pope, distilling, el-hol,\n",
      "Nearest to is: was, are, has, contains, 19°, esoteric, remains, policemen,\n",
      "Nearest to ,: ;, ., herman, describing, 20,700, stockings, compiègne, conceptualization,\n",
      "Nearest to which: that, but, who, where, still, greater, glazunov, bowls,\n",
      "Nearest to its: their, the, his, her, alexei, bharat, roundhay, madhhabs,\n",
      "Nearest to the: its, a, each, another, any, alexei, her, agriculturalists,\n",
      "Nearest to or: and, tripura, like, tyrannosaurid, robe, maya, two-front, groundwork,\n",
      "Nearest to he: she, it, they, o'dea, olbers, chaperones, nederlander, tiber,\n",
      "Nearest to city: emphasises, persistence, grease, reversed, infidels, corporate, nuys, doe,\n",
      "Nearest to (: montt, parenthood, malibu, magill, entrenched, patrician, 1640., unintentionally,\n",
      "Average loss at step 22000: 2.442514\n",
      "Average loss at step 24000: 2.443107\n",
      "Average loss at step 26000: 2.421056\n",
      "Average loss at step 28000: 2.327259\n",
      "Average loss at step 30000: 2.267714\n",
      "Nearest to have: has, had, limestones, challenges, having, designate, depose, mean,\n",
      "Nearest to other: various, year., tana, many, gebhart, edgeworth, technologies, striped,\n",
      "Nearest to had: has, have, having, was, sweetener, chiefly, were, 1740,\n",
      "Nearest to 's: her, his, toucouleurs, inverness, voivode, glorified, desist, osmotic,\n",
      "Nearest to their: its, his, her, iudeu, the, overruns, tempos, these,\n",
      "Nearest to were: are, was, be, grows, becomes, stalks, kazan, had,\n",
      "Nearest to on: feynman, knowable, virions, aa, monologues, el-hol, claire, upon,\n",
      "Nearest to is: was, are, has, remains, 19°, became, contains, esoteric,\n",
      "Nearest to ,: ;, abbott, herman, describing, jost, cartier, archaeal, compiègne,\n",
      "Nearest to which: where, but, who, that, glazunov, irises, 312, including,\n",
      "Nearest to its: their, the, his, alexei, her, bharat, methodological, roundhay,\n",
      "Nearest to the: its, another, agriculturalists, every, rock-mass, their, any, cordial,\n",
      "Nearest to or: and, like, tripura, two-front, whisky, maya, than, embraces,\n",
      "Nearest to he: she, it, they, o'dea, chaperones, olbers, buganda, tiber,\n",
      "Nearest to city: emphasises, reversed, persistence, popularizing, doe, grease, nuys, corporate,\n",
      "Nearest to (: montt, parenthood, malibu, magill, 1640., entrenched, revolutionized, unintentionally,\n",
      "Average loss at step 32000: 2.268012\n",
      "Average loss at step 34000: 2.261286\n",
      "Average loss at step 36000: 2.241489\n",
      "Average loss at step 38000: 2.210448\n",
      "Average loss at step 40000: 2.226815\n",
      "Nearest to have: has, had, having, limestones, challenges, designate, mean, cyclonic,\n",
      "Nearest to other: various, year., gebhart, these, technologies, tana, edgeworth, antisubmarine,\n",
      "Nearest to had: has, have, having, was, 1740, sweetener, bug, were,\n",
      "Nearest to 's: s, glorified, toucouleurs, voivode, oligodendrocytes, inverness, his, desist,\n",
      "Nearest to their: its, his, her, iudeu, tempos, op, these, human-caused,\n",
      "Nearest to were: are, was, grows, be, stalks, pressurised, reigning, kazan,\n",
      "Nearest to on: upon, feynman, keyboard, knowable, pope, distilling, virions, el-hol,\n",
      "Nearest to is: was, are, remains, contains, has, 19°, intermarriages, became,\n",
      "Nearest to ,: ;, ., jost, herman, describing, abbott, —, magnify,\n",
      "Nearest to which: where, but, that, who, including, irises, especially, pali,\n",
      "Nearest to its: their, his, the, alexei, bharat, nanwang, pitton, artistic,\n",
      "Nearest to the: another, its, rock-mass, every, grainger, a, any, each,\n",
      "Nearest to or: and, like, tripura, embraces, cristae, two-front, groundwork, tyrannosaurid,\n",
      "Nearest to he: she, it, they, o'dea, olbers, chaperones, buganda, tiber,\n",
      "Nearest to city: reversed, country, persistence, corporate, grease, emphasises, nuys, doe,\n",
      "Nearest to (: montt, parenthood, malibu, entrenched, revolutionized, magill, 1640., doorway,\n",
      "Average loss at step 42000: 2.206654\n",
      "Average loss at step 44000: 2.219112\n",
      "Average loss at step 46000: 2.198191\n",
      "Average loss at step 48000: 2.180325\n",
      "Average loss at step 50000: 2.211664\n",
      "Nearest to have: has, had, having, mean, limestones, allow, challenges, jeremiah,\n",
      "Nearest to other: various, tana, year., technologies, gebhart, these, prescribed, kinematical,\n",
      "Nearest to had: has, have, having, 1740, bug, humanitarian, was, sweetener,\n",
      "Nearest to 's: s, glorified, his, toucouleurs, oligodendrocytes, inverness, wojsko, desist,\n",
      "Nearest to their: its, his, her, tempos, iudeu, human-caused, overruns, op,\n",
      "Nearest to were: are, was, be, grows, stalks, reigning, being, been,\n",
      "Nearest to on: upon, knowable, feynman, distilling, cpgb, virions, el-hol, aa,\n",
      "Nearest to is: was, remains, are, contains, has, 19°, became, esoteric,\n",
      "Nearest to ,: ;, —, ., abbott, prorogued, jost, –, 95.6,\n",
      "Nearest to which: where, but, that, who, including, glazunov, pali, irises,\n",
      "Nearest to its: their, his, the, artistic, alexei, bharat, her, nanwang,\n",
      "Nearest to the: its, every, a, another, rock-mass, harriers, reorganisation, rho,\n",
      "Nearest to or: and, tripura, like, embraces, two-front, groundwork, than, prospectors,\n",
      "Nearest to he: she, it, they, o'dea, olbers, buganda, hitler, tiber,\n",
      "Nearest to city: country, emphasises, doe, norfolk, vesicular, territory, nuys, grease,\n",
      "Nearest to (: montt, parenthood, malibu, entrenched, butyric, tonne, magill, power-sharing,\n",
      "Average loss at step 52000: 2.144354\n",
      "Average loss at step 54000: 2.109554\n",
      "Average loss at step 56000: 2.088652\n",
      "Average loss at step 58000: 2.075117\n",
      "Average loss at step 60000: 2.056750\n",
      "Nearest to have: has, had, having, mean, limestones, allow, gesner, designate,\n",
      "Nearest to other: various, tana, gebhart, year., antisubmarine, marrying, technologies, pathophysiology,\n",
      "Nearest to had: has, have, having, 1740, was, bug, sweetener, humanitarian,\n",
      "Nearest to 's: s, toucouleurs, oligodendrocytes, wojsko, inverness, glorified, harrowing, osmotic,\n",
      "Nearest to their: its, his, her, tempos, our, human-caused, iudeu, the,\n",
      "Nearest to were: are, was, grows, stalks, be, reigning, being, pressurised,\n",
      "Nearest to on: upon, knowable, distilling, feynman, virions, claire, el-hol, aa,\n",
      "Nearest to is: was, remains, are, contains, has, 19°, intermarriages, esoteric,\n",
      "Nearest to ,: ;, —, herman, describing, ., strongholds, –, prorogued,\n",
      "Nearest to which: where, but, that, who, primarily, whom, pali, irises,\n",
      "Nearest to its: their, his, artistic, bharat, alexei, methodological, her, nanwang,\n",
      "Nearest to the: every, another, shotgun, each, grainger, its, luxuriant, their,\n",
      "Nearest to or: and, tripura, embraces, franklinton, like, prospectors, than, whisky,\n",
      "Nearest to he: she, it, they, o'dea, tolkien, hitler, olbers, buganda,\n",
      "Nearest to city: country, region, doe, emphasises, territory, area, nuys, movie,\n",
      "Nearest to (: montt, entrenched, parenthood, power-sharing, tonne, fetch, -, blew,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 62000: 2.086671\n",
      "Average loss at step 64000: 2.035246\n",
      "Average loss at step 66000: 2.064289\n",
      "Average loss at step 68000: 2.039306\n",
      "Average loss at step 70000: 2.033401\n",
      "Nearest to have: has, had, having, allow, mean, limestones, cyclonic, gesner,\n",
      "Nearest to other: various, tana, individual, gebhart, prescribed, year., technologies, antisubmarine,\n",
      "Nearest to had: has, have, having, 1740, bug, humanitarian, sweetener, was,\n",
      "Nearest to 's: s, oligodendrocytes, dirac, glorified, toucouleurs, desist, my, grand-bassam,\n",
      "Nearest to their: its, his, her, our, op, human-caused, tempos, powiśle,\n",
      "Nearest to were: are, was, grows, been, stalks, becomes, being, be,\n",
      "Nearest to on: upon, knowable, distilling, el-hol, constant-current, feynman, cpgb, claire,\n",
      "Nearest to is: was, remains, contains, are, has, 19°, intermarriages, esoteric,\n",
      "Nearest to ,: —, ;, ., prorogued, –, strongholds, vinohrady, halfpence,\n",
      "Nearest to which: where, but, that, who, primarily, pali, whom, irises,\n",
      "Nearest to its: their, his, artistic, alexei, our, the, bharat, her,\n",
      "Nearest to the: every, rock-mass, its, millions, a, our, another, rho,\n",
      "Nearest to or: and, embraces, groundwork, prospectors, tyrannosaurid, tripura, cristae, phl,\n",
      "Nearest to he: she, it, they, hitler, o'dea, tolkien, olbers, buganda,\n",
      "Nearest to city: country, doe, town, movie, region, emphasises, grease, nuys,\n",
      "Nearest to (: montt, parenthood, entrenched, blew, malibu, -, tonne, power-sharing,\n",
      "Average loss at step 72000: 2.044690\n",
      "Average loss at step 74000: 2.021386\n",
      "Average loss at step 76000: 2.060159\n",
      "Average loss at step 78000: 2.003408\n",
      "Average loss at step 80000: 1.951999\n",
      "Nearest to have: has, had, having, limestones, allow, mean, gesner, cyclonic,\n",
      "Nearest to other: various, tana, individual, year., kinematical, prescribed, gebhart, rouge,\n",
      "Nearest to had: have, has, having, 1740, bug, sweetener, limestones, humanitarian,\n",
      "Nearest to 's: s, desist, oligodendrocytes, toucouleurs, dirac, wojsko, voivode, rosewood,\n",
      "Nearest to their: its, his, her, tempos, our, op, human-caused, elevated,\n",
      "Nearest to were: are, was, grows, reigning, stalks, been, be, being,\n",
      "Nearest to on: upon, knowable, distilling, el-hol, feynman, virions, claire, constant-current,\n",
      "Nearest to is: was, remains, are, contains, has, 19°, intermarriages, becomes,\n",
      "Nearest to ,: ;, —, -, –, herman, 08:00, cartier, masurian,\n",
      "Nearest to which: where, but, whom, that, primarily, e.g., who, irises,\n",
      "Nearest to its: their, his, artistic, the, alexei, methodological, bharat, our,\n",
      "Nearest to the: agriculturalists, its, every, hamam, shotgun, single-stranded, another, nearing,\n",
      "Nearest to or: and, embraces, prospectors, cristae, groundwork, whisky, franklinton, offside,\n",
      "Nearest to he: she, it, they, o'dea, tolkien, hitler, churchill, olbers,\n",
      "Nearest to city: country, region, town, doe, cities, emphasises, pratica, territory,\n",
      "Nearest to (: montt, tonne, blew, fetch, malibu, -, parenthood, power-sharing,\n",
      "Average loss at step 82000: 1.958124\n",
      "Average loss at step 84000: 1.951141\n",
      "Average loss at step 86000: 1.951372\n",
      "Average loss at step 88000: 1.931524\n",
      "Average loss at step 90000: 1.929979\n",
      "Nearest to have: has, had, having, mean, allow, limestones, gesner, designate,\n",
      "Nearest to other: various, individual, tana, kinematical, pathophysiology, year., gebhart, these,\n",
      "Nearest to had: has, have, having, 1740, bug, sweetener, departed, earn,\n",
      "Nearest to 's: s, oligodendrocytes, wojsko, dirac, glorified, inverness, desist, toucouleurs,\n",
      "Nearest to their: its, his, her, our, tempos, human-caused, powiśle, elevated,\n",
      "Nearest to were: are, was, stalks, grows, reigning, physiographic, been, kazan,\n",
      "Nearest to on: upon, feynman, constant-current, knowable, distilling, claire, el-hol, cpgb,\n",
      "Nearest to is: was, remains, are, contains, 19°, has, intermarriages, becomes,\n",
      "Nearest to ,: —, ;, ., jost, herman, –, strongholds, admit,\n",
      "Nearest to which: where, but, whom, that, e.g., primarily, what, irises,\n",
      "Nearest to its: their, his, artistic, alexei, bharat, methodological, the, nanwang,\n",
      "Nearest to the: its, agriculturalists, novotná, a, luxuriant, rock-mass, grainger, corruption,\n",
      "Nearest to or: embraces, and, prospectors, cristae, groundwork, phl, tyrannosaurid, two-front,\n",
      "Nearest to he: she, it, they, tolkien, olbers, hitler, o'dea, unchanged,\n",
      "Nearest to city: country, region, town, cities, doe, movie, territory, grease,\n",
      "Nearest to (: montt, tonne, fetch, revolutionized, entrenched, malibu, parenthood, -,\n",
      "Average loss at step 92000: 1.930753\n",
      "Average loss at step 94000: 1.919240\n",
      "Average loss at step 96000: 1.933512\n",
      "Average loss at step 98000: 1.913997\n",
      "Average loss at step 100000: 1.931318\n",
      "Nearest to have: has, having, had, limestones, mean, allow, gesner, contain,\n",
      "Nearest to other: various, tana, individual, kinematical, pathophysiology, rouge, technologies, gebhart,\n",
      "Nearest to had: have, has, having, 1740, bug, humanitarian, sweetener, departed,\n",
      "Nearest to 's: s, my, glorified, desist, toucouleurs, inverness, grand-bassam, oligodendrocytes,\n",
      "Nearest to their: its, his, her, tempos, human-caused, trax, our, powiśle,\n",
      "Nearest to were: are, was, stalks, been, reigning, be, grows, themselves,\n",
      "Nearest to on: upon, knowable, constant-current, distilling, el-hol, claire, cpgb, preachers,\n",
      "Nearest to is: was, remains, are, contains, has, 19°, becomes, toppings,\n",
      "Nearest to ,: —, ;, herman, –, ., stockings, abbott, jost,\n",
      "Nearest to which: where, but, whom, that, e.g., primarily, who, pali,\n",
      "Nearest to its: their, his, artistic, alexei, bharat, powiśle, nanwang, the,\n",
      "Nearest to the: harriers, cryptography, 12.8, a, mekhloufi, another, its, every,\n",
      "Nearest to or: prospectors, and, embraces, groundwork, cristae, franklinton, phl, milione,\n",
      "Nearest to he: she, they, tolkien, it, tesla, o'dea, hitler, ruth,\n",
      "Nearest to city: country, region, town, doe, cities, vancouver, territory, grease,\n",
      "Nearest to (: montt, blew, tonne, fetch, malibu, entrenched, butyric, magill,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "cbow_loss_unigram = []\n",
    "\n",
    "# ConfigProto is a way of providing various configuration settings \n",
    "# required to execute the graph\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as session:\n",
    "    \n",
    "    # Initialize the variables in the graph\n",
    "    tf.global_variables_initializer().run()\n",
    "    print('Initialized')\n",
    "    \n",
    "    average_loss = 0\n",
    "    \n",
    "    # Train the Word2vec model for num_step iterations\n",
    "    for step in range(num_steps):\n",
    "        \n",
    "        # Generate a single batch of data\n",
    "        batch_data, batch_labels = generate_batch_cbow(batch_size, window_size)\n",
    "        \n",
    "        # Populate the feed_dict and run the optimizer (minimize loss)\n",
    "        # and compute the loss\n",
    "        feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "        _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "        \n",
    "        # Update the average loss variable\n",
    "        average_loss += l\n",
    "        \n",
    "        if (step+1) % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss = average_loss / 2000\n",
    "                # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            cbow_loss_unigram.append(average_loss)\n",
    "            print('Average loss at step %d: %f' % (step+1, average_loss))\n",
    "            average_loss = 0\n",
    "            \n",
    "        # Evaluating validation set word similarities\n",
    "        if (step+1) % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            # Here we compute the top_k closest words for a given validation word\n",
    "            # in terms of the cosine distance\n",
    "            # We do this for all the words in the validation set\n",
    "            # Note: This is an expensive step\n",
    "            for i in range(valid_size):\n",
    "                valid_word = reverse_dictionary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log = 'Nearest to %s:' % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = reverse_dictionary[nearest[k]]\n",
    "                    log = '%s %s,' % (log, close_word)\n",
    "                print(log)\n",
    "    cbow_final_embeddings = normalized_embeddings.eval()\n",
    "    \n",
    "    \n",
    "with open('cbow_unigram_losses.csv', 'wt') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    writer.writerow(cbow_loss_unigram)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subsampling the Frequent words\n",
    "\n",
    "This is important as the most-frequent words such as \"in\", \"a\", \"the\" do not add a significant value to word embeddings. For example, a training input output tuple (France, Paris) has more information than (France, The). So if we can avoid such frequent words, it can help to boost the quality of word vectors. \n",
    "\n",
    "Therefore we sample each word $w_i$ with a probability $P(w_i) = 1 - \\sqrt{\\frac{t}{f(w_i)}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Word Sequence with Subsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropped 4% words (143768 words) in total...\n",
      "Dropped Examples:  ['the', 'of', 'the', 'the', 'the', 'the', 'of', 'the', 'the', 'of', 'the', 'the', ',', 'the', 'the', '.', 'the', ',', ',', 'the']\n",
      "\n",
      "Original data:  ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing', 'the', 'opinions', 'or', 'behavior', 'of', 'large', 'numbers', 'of', 'people', '.']\n",
      "\n",
      "Subsampled data:  ['propaganda', 'is', 'a', 'concerted', 'set', 'of', 'messages', 'aimed', 'at', 'influencing', 'the', 'opinions', 'or', 'behavior', 'of', 'large', 'numbers', 'of', 'people', '.']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "subsampled_data = []\n",
    "drop_count = 0\n",
    "drop_examples = []\n",
    "\n",
    "# Here we traverse through the data and drop irrelavent words\n",
    "# according to the subsampling probability \n",
    "for w_i in data:\n",
    "    # Note that the paper uses t=1e-5\n",
    "    # This is fine when using a normalized frequency of words\n",
    "    # But we are using raw frequencies so we set t=1e5\n",
    "    p_w_i = 1 - np.sqrt(1e5/word_count_dictionary[w_i])\n",
    "    \n",
    "    if np.random.random() < p_w_i:\n",
    "        drop_count += 1\n",
    "        drop_examples.append(reverse_dictionary[w_i])\n",
    "    else:\n",
    "        subsampled_data.append(w_i)\n",
    "        \n",
    "# Print some statistics\n",
    "print('Dropped %d%% words (%d words) in total...'%(drop_count*100.0/len(data),drop_count))\n",
    "print('Dropped Examples: ', drop_examples[:20])\n",
    "print('\\nOriginal data: ',[reverse_dictionary[w_i] for w_i in data[:20]])\n",
    "print('\\nSubsampled data: ',[reverse_dictionary[w_i] for w_i in subsampled_data[:20]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running CBOW with Unigram Sampling + Subsampling\n",
    "\n",
    "Here we run the CBOW with unigram sampling and subsampling which we defined above. Specifically, we first initialize variables, and then train the algorithm for many steps (`num_steps`). And every few steps we evaluate the algorithm on a fixed validation set and print out the words that appear to be closest for a given set of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step 2000: 3.426986\n",
      "Average loss at step 4000: 2.917407\n",
      "Average loss at step 6000: 2.767598\n",
      "Average loss at step 8000: 2.688828\n",
      "Average loss at step 10000: 2.643141\n",
      "Nearest to have: has, be, had, hatred, cec, labiovelar, do, agglomeration,\n",
      "Nearest to other: impeachment, idiocy, vehicles, mexican-american, meat, municipal, 38, more,\n",
      "Nearest to had: has, have, was, natal, high-profile, |feb_rec_hi_°c, net, imaged,\n",
      "Nearest to 's: charles, diamonds, epitheta, nep, oatmeal, -dir4, cosmopolitan, clap,\n",
      "Nearest to their: its, his, the, halicarnassus, her, 1806, botany, disappointment,\n",
      "Nearest to were: are, was, would, porsche, 1868, karachi, potent, bhed,\n",
      "Nearest to on: puma, forfeda, marge, auto, an/sps-67, files, krenz, |sep_lo_°c,\n",
      "Nearest to is: was, are, negotiation, has, negatively, accrete, macgreevy, compacted,\n",
      "Nearest to ,: ;, tupolev, ., 18, grew, -, =, petros,\n",
      "Nearest to which: assisi, but, reale, tamil, fritz, leftover, light-weight, hiding,\n",
      "Nearest to its: their, the, his, algorithm, disclosed, ingrained, programmer, telecommunication,\n",
      "Nearest to the: its, their, an, a, any, this, tombalbaye, minami,\n",
      "Nearest to or: and, great-granddaughter, beverages, aberrations, tiber, yellowstone, 337, penal,\n",
      "Nearest to he: it, she, highs, they, comgaill, dgla, alberta, luxembourgish,\n",
      "Nearest to city: acoustic, gaye/terrell, communications, south, exonerated, capitolio, four-foot, hannibal,\n",
      "Nearest to (: provenance, tutor, stormy, microbes, reconstructionists, polyps, upheavals, creeley,\n",
      "Average loss at step 12000: 2.580117\n",
      "Average loss at step 14000: 2.544546\n",
      "Average loss at step 16000: 2.529346\n",
      "Average loss at step 18000: 2.494829\n",
      "Average loss at step 20000: 2.480949\n",
      "Nearest to have: has, had, be, hatred, cec, agglomeration, residues, expenditures,\n",
      "Nearest to other: vehicles, these, impeachment, idiocy, municipal, mexican-american, inhabitant, some,\n",
      "Nearest to had: has, have, natal, |feb_rec_hi_°c, was, net, high-profile, steppe,\n",
      "Nearest to 's: epitheta, charles, diamonds, rdl, typified, bulguksa, his, theo,\n",
      "Nearest to their: its, his, her, the, these, 1806, halicarnassus, raged,\n",
      "Nearest to were: are, was, potent, be, porsche, karachi, hamm, had,\n",
      "Nearest to on: puma, marge, krenz, forfeda, auto, toward, an/sps-67, furious,\n",
      "Nearest to is: was, are, has, negotiation, negatively, became, macgreevy, accrete,\n",
      "Nearest to ,: ., ;, tupolev, 18, crown-of-thorns, northumbrians, infighting, .the,\n",
      "Nearest to which: but, assisi, including, reale, leftover, who, fritz, except,\n",
      "Nearest to its: their, his, the, her, disclosed, telecommunication, algorithm, programmer,\n",
      "Nearest to the: a, its, an, any, their, ching, dualism, coaling,\n",
      "Nearest to or: and, arcminute, beverages, nansen, great-granddaughter, pick, aberrations, than,\n",
      "Nearest to he: she, it, they, highs, dgla, comgaill, dietitians, alberta,\n",
      "Nearest to city: south, gaye/terrell, hannibal, capitolio, acoustic, region, ingesting, franceville,\n",
      "Nearest to (: provenance, seventeen-year-old, tutor, =, upheavals, welds, stagnating, stormy,\n",
      "Average loss at step 22000: 2.455002\n",
      "Average loss at step 24000: 2.438396\n",
      "Average loss at step 26000: 2.411332\n",
      "Average loss at step 28000: 2.334313\n",
      "Average loss at step 30000: 2.289127\n",
      "Nearest to have: has, had, be, cec, having, hatred, agglomeration, bargaining,\n",
      "Nearest to other: vehicles, these, impeachment, idiocy, various, municipal, burgher, environmental,\n",
      "Nearest to had: has, have, was, |feb_rec_hi_°c, having, net, allosaurus, were,\n",
      "Nearest to 's: charles, his, epitheta, typified, her, jovial, claria, rdl,\n",
      "Nearest to their: its, his, her, the, these, our, halicarnassus, calderón,\n",
      "Nearest to were: are, was, potent, being, be, mileva, had, karachi,\n",
      "Nearest to on: upon, puma, marge, toward, forfeda, krenz, cornish, auto,\n",
      "Nearest to is: was, are, has, became, accrete, negotiation, macgreevy, negatively,\n",
      "Nearest to ,: ;, ., .the, crown-of-thorns, tupolev, 18, -, lecturers,\n",
      "Nearest to which: but, assisi, including, reale, leftover, where, except, who,\n",
      "Nearest to its: their, the, his, disclosed, telecommunication, 90.5, german-soviet, her,\n",
      "Nearest to the: its, their, any, ching, tombalbaye, dualism, my, coaling,\n",
      "Nearest to or: and, arcminute, beverages, tiber, nansen, than, friction, great-granddaughter,\n",
      "Nearest to he: she, it, they, highs, dgla, dietitians, experimentation, comgaill,\n",
      "Nearest to city: region, south, gaye/terrell, urban, meridian, hannibal, ingesting, four-foot,\n",
      "Nearest to (: provenance, =, upheavals, tutor, gigas, stormy, consolidation, microbes,\n",
      "Average loss at step 32000: 2.262068\n",
      "Average loss at step 34000: 2.249331\n",
      "Average loss at step 36000: 2.230311\n",
      "Average loss at step 38000: 2.243265\n",
      "Average loss at step 40000: 2.214014\n",
      "Nearest to have: has, had, having, cec, be, agglomeration, hatred, hold,\n",
      "Nearest to other: vehicles, various, these, environmental, municipal, idiocy, impeachment, burgher,\n",
      "Nearest to had: has, have, having, was, |feb_rec_hi_°c, spool, natal, imaged,\n",
      "Nearest to 's: epitheta, s, his, rdl, typified, diamonds, claria, theo,\n",
      "Nearest to their: its, his, her, our, calderón, halicarnassus, these, the,\n",
      "Nearest to were: are, was, being, be, potent, have, mileva, porsche,\n",
      "Nearest to on: upon, puma, marge, toward, auto, forfeda, krenz, an/sps-67,\n",
      "Nearest to is: was, are, has, became, negatively, macgreevy, accrete, contains,\n",
      "Nearest to ,: ;, tupolev, ., .the, m1903, schiphol, unaware, corporatist,\n",
      "Nearest to which: but, assisi, where, including, leftover, reale, este, whose,\n",
      "Nearest to its: their, his, the, gorgosaurus, telecommunication, disclosed, 90.5, programmer,\n",
      "Nearest to the: ching, any, its, coaling, our, a, tombalbaye, 1484,\n",
      "Nearest to or: arcminute, beverages, and, than, tiber, nansen, friction, wampum,\n",
      "Nearest to he: she, it, they, highs, comgaill, experimentation, dgla, assembles,\n",
      "Nearest to city: region, stigmas, gaye/terrell, hannibal, south, meridian, capitolio, urban,\n",
      "Nearest to (: provenance, upheavals, tutor, welds, polyps, 2300, n-terminus, creeley,\n",
      "Average loss at step 42000: 2.210325\n",
      "Average loss at step 44000: 2.194265\n",
      "Average loss at step 46000: 2.197891\n",
      "Average loss at step 48000: 2.178514\n",
      "Average loss at step 50000: 2.167064\n",
      "Nearest to have: has, had, having, agglomeration, cec, hold, ghirlandaio, 9.,\n",
      "Nearest to other: various, vehicles, environmental, these, phagwa, impeachment, individual, burgher,\n",
      "Nearest to had: has, have, having, spool, were, was, net, |feb_rec_hi_°c,\n",
      "Nearest to 's: epitheta, s, kampong, prefectural, claria, typified, rdl, his,\n",
      "Nearest to their: its, his, her, our, halicarnassus, calderón, 1125, dairyland,\n",
      "Nearest to were: are, was, being, potent, be, had, gediminas, winwaed,\n",
      "Nearest to on: upon, toward, puma, marge, forfeda, krenz, grinch, |sep_lo_°c,\n",
      "Nearest to is: was, are, has, became, contains, macgreevy, negatively, remained,\n",
      "Nearest to ,: ;, tupolev, ., schiphol, m1903, .the, noblewomen, shamans,\n",
      "Nearest to which: but, assisi, where, including, leftover, whose, that, reale,\n",
      "Nearest to its: their, his, telecommunication, 90.5, german-soviet, gorgosaurus, disclosed, the,\n",
      "Nearest to the: a, any, tombalbaye, ching, dualism, coaling, another, its,\n",
      "Nearest to or: and, beverages, arcminute, nansen, than, tiber, wampum, aberrations,\n",
      "Nearest to he: she, it, they, highs, championed, experimentation, psychiatrist, dietitians,\n",
      "Nearest to city: region, south, country, urban, meridian, gaye/terrell, ingesting, hannibal,\n",
      "Nearest to (: provenance, upheavals, tutor, poesis, gigas, welds, commonalities, 2300,\n",
      "Average loss at step 52000: 2.169739\n",
      "Average loss at step 54000: 2.123463\n",
      "Average loss at step 56000: 2.077123\n",
      "Average loss at step 58000: 2.080858\n",
      "Average loss at step 60000: 2.068473\n",
      "Nearest to have: has, had, having, cec, hold, hatred, agglomeration, develop,\n",
      "Nearest to other: vehicles, various, environmental, individual, phagwa, these, municipal, impeachment,\n",
      "Nearest to had: has, have, having, was, spool, |feb_rec_hi_°c, were, 1779,\n",
      "Nearest to 's: epitheta, rdl, s, claria, typified, kampong, clap, his,\n",
      "Nearest to their: its, his, her, our, calderón, halicarnassus, 1125, critiquing,\n",
      "Nearest to were: are, was, being, potent, mileva, gediminas, be, xfree86,\n",
      "Nearest to on: upon, toward, puma, marge, forfeda, imitates, cornish, paull,\n",
      "Nearest to is: was, are, has, contains, became, becomes, negatively, macgreevy,\n",
      "Nearest to ,: ;, tupolev, ., prosperous, abysmally, m1903, anomalous, reborn,\n",
      "Nearest to which: but, where, assisi, leftover, including, whose, reale, except,\n",
      "Nearest to its: their, his, the, telecommunication, gorgosaurus, 90.5, seducing, her,\n",
      "Nearest to the: dualism, its, ching, pluto, coaling, tombalbaye, our, hundreds,\n",
      "Nearest to or: beverages, arcminute, and, nansen, wampum, friction, tiber, than,\n",
      "Nearest to he: she, it, they, experimentation, highs, championed, dgla, supplemental,\n",
      "Nearest to city: region, urban, country, gaye/terrell, meridian, south, stigmas, tunneling,\n",
      "Nearest to (: upheavals, provenance, tutor, welds, microbes, poesis, =, polyps,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step 62000: 2.071420\n",
      "Average loss at step 64000: 2.037744\n",
      "Average loss at step 66000: 2.048317\n",
      "Average loss at step 68000: 2.034745\n",
      "Average loss at step 70000: 2.038459\n",
      "Nearest to have: has, had, having, agglomeration, hatred, hold, cec, 9.,\n",
      "Nearest to other: various, vehicles, individual, environmental, phagwa, these, pili, unrest,\n",
      "Nearest to had: have, has, having, were, spool, was, supervisor, remi,\n",
      "Nearest to 's: epitheta, s, claria, ’, typified, kampong, rdl, jacobins,\n",
      "Nearest to their: its, his, her, our, halicarnassus, calderón, 1125, critiquing,\n",
      "Nearest to were: are, was, potent, being, had, mileva, gediminas, winwaed,\n",
      "Nearest to on: upon, toward, marge, puma, auto, furious, krenz, cornish,\n",
      "Nearest to is: was, are, contains, has, became, negatively, becomes, remained,\n",
      "Nearest to ,: ;, tupolev, ., m1903, anomalous, lecturers, cog, schiphol,\n",
      "Nearest to which: where, but, assisi, whose, including, leftover, reale, that,\n",
      "Nearest to its: their, his, 90.5, telecommunication, her, gorgosaurus, seducing, disclosed,\n",
      "Nearest to the: coaling, our, 1484, hassan, dualism, pluto, ching, reinvented,\n",
      "Nearest to or: beverages, arcminute, and, nansen, tiber, wampum, 337, than,\n",
      "Nearest to he: she, it, they, finally, dgla, defoe, marx, supplemental,\n",
      "Nearest to city: region, meridian, urban, gaye/terrell, south, town, country, pratica,\n",
      "Nearest to (: provenance, upheavals, polyps, tutor, welds, galmudug, microbes, commonalities,\n",
      "Average loss at step 72000: 2.023878\n",
      "Average loss at step 74000: 2.026321\n",
      "Average loss at step 76000: 2.045310\n",
      "Average loss at step 78000: 2.001861\n",
      "Average loss at step 80000: 1.983738\n",
      "Nearest to have: has, had, having, agglomeration, cec, develop, hold, allow,\n",
      "Nearest to other: various, individual, environmental, vehicles, phagwa, impeachment, pili, these,\n",
      "Nearest to had: have, has, having, spool, were, 1779, enlist, remi,\n",
      "Nearest to 's: s, epitheta, kampong, ’, typified, jacobins, claria, clap,\n",
      "Nearest to their: its, his, her, our, calderón, halicarnassus, 1125, critiquing,\n",
      "Nearest to were: are, was, potent, being, mileva, had, hernando, gediminas,\n",
      "Nearest to on: upon, toward, puma, marge, huston, paull, cornish, imitates,\n",
      "Nearest to is: was, are, contains, becomes, has, negatively, became, remained,\n",
      "Nearest to ,: ;, tupolev, m1903, –, —, cog, .the, .,\n",
      "Nearest to which: but, where, assisi, whose, leftover, including, reale, except,\n",
      "Nearest to its: their, his, telecommunication, 90.5, german-soviet, gorgosaurus, disclosed, the,\n",
      "Nearest to the: ching, reinvented, hundreds, its, dualism, 1484, pishpek, lobectomy,\n",
      "Nearest to or: beverages, and, arcminute, tiber, nansen, wampum, than, 337,\n",
      "Nearest to he: she, it, they, tesla, marx, codifying, churchill, hitler,\n",
      "Nearest to city: region, urban, meridian, gaye/terrell, town, south, pratica, stigmas,\n",
      "Nearest to (: provenance, upheavals, commonalities, tutor, gigas, welds, galmudug, 2300,\n",
      "Average loss at step 82000: 1.936912\n",
      "Average loss at step 84000: 1.958798\n",
      "Average loss at step 86000: 1.940759\n",
      "Average loss at step 88000: 1.947513\n",
      "Average loss at step 90000: 1.903370\n",
      "Nearest to have: has, had, having, cec, hold, hatred, agglomeration, develop,\n",
      "Nearest to other: various, individual, environmental, vehicles, impeachment, burgher, phagwa, pili,\n",
      "Nearest to had: has, have, having, spool, supervisor, enlist, 1779, state-sponsored,\n",
      "Nearest to 's: s, epitheta, kampong, ’, prefectural, claria, 1670, bulguksa,\n",
      "Nearest to their: its, his, her, our, halicarnassus, calderón, 1125, my,\n",
      "Nearest to were: are, was, potent, being, vaults, porsche, gediminas, mileva,\n",
      "Nearest to on: upon, toward, marge, puma, imitates, auto, cornish, furious,\n",
      "Nearest to is: was, are, contains, has, becomes, negatively, makes, became,\n",
      "Nearest to ,: ;, tupolev, ., schiphol, aten, cog, strategist, –,\n",
      "Nearest to which: where, whose, but, assisi, including, leftover, whom, reale,\n",
      "Nearest to its: their, his, gorgosaurus, the, telecommunication, 90.5, german-soviet, programmer,\n",
      "Nearest to the: dualism, its, coaling, tombalbaye, ching, lobectomy, sigismunda, purchasing,\n",
      "Nearest to or: beverages, arcminute, and, friction, wampum, tiber, nansen, short-story,\n",
      "Nearest to he: she, they, it, tesla, tolkien, marx, galileo, wittgenstein,\n",
      "Nearest to city: region, town, gaye/terrell, meridian, urban, stigmas, adepts, south,\n",
      "Nearest to (: upheavals, provenance, welds, commonalities, poesis, 2300, tutor, galmudug,\n",
      "Average loss at step 92000: 1.929010\n",
      "Average loss at step 94000: 1.926096\n",
      "Average loss at step 96000: 1.922383\n",
      "Average loss at step 98000: 1.913462\n",
      "Average loss at step 100000: 1.920973\n",
      "Nearest to have: has, had, having, agglomeration, hold, 9., allow, develop,\n",
      "Nearest to other: various, individual, environmental, vehicles, phagwa, impeachment, pili, these,\n",
      "Nearest to had: has, have, having, spool, were, enlist, supervisor, state-sponsored,\n",
      "Nearest to 's: s, epitheta, prefectural, claria, ’, kampong, jacobins, typified,\n",
      "Nearest to their: its, his, her, our, halicarnassus, calderón, critiquing, my,\n",
      "Nearest to were: are, was, being, potent, been, vaults, had, porsche,\n",
      "Nearest to on: upon, toward, marge, puma, auto, |sep_lo_°c, groundstrokes, settings,\n",
      "Nearest to is: was, are, contains, becomes, makes, negatively, has, macgreevy,\n",
      "Nearest to ,: ;, tupolev, —, –, ., ocho, .the, transpo,\n",
      "Nearest to which: whose, assisi, where, whom, but, leftover, including, reale,\n",
      "Nearest to its: their, his, her, gorgosaurus, 90.5, whose, german-soviet, telecommunication,\n",
      "Nearest to the: dualism, hassan, coaling, pluto, twenty-eight, ching, reinvented, slavery,\n",
      "Nearest to or: beverages, arcminute, nansen, than, wampum, and, tiber, veering,\n",
      "Nearest to he: she, they, tesla, galileo, it, tolkien, marx, hitler,\n",
      "Nearest to city: region, meridian, urban, gaye/terrell, town, four-foot, stigmas, adepts,\n",
      "Nearest to (: provenance, upheavals, welds, 2300, commonalities, tutor, gigas, poesis,\n"
     ]
    }
   ],
   "source": [
    "num_steps = 100001\n",
    "cbow_loss_unigram_subsampled = []\n",
    "\n",
    "\n",
    "# ConfigProto is a way of providing various configuration settings \n",
    "# required to execute the graph\n",
    "with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as session:\n",
    "    \n",
    "    # Initialize the variables in the graph\n",
    "    tf.global_variables_initializer().run()\n",
    "    print('Initialized')\n",
    "    \n",
    "    average_loss = 0\n",
    "    \n",
    "    # Train the Word2vec model for num_step iterations\n",
    "    for step in range(num_steps):\n",
    "        \n",
    "        # Generate a single batch of data\n",
    "        batch_data, batch_labels = generate_batch_cbow(batch_size, window_size)\n",
    "        \n",
    "        # Populate the feed_dict and run the optimizer (minimize loss)\n",
    "        # and compute the loss\n",
    "        feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
    "        _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "        \n",
    "        # Update the average loss variable\n",
    "        average_loss += l\n",
    "        \n",
    "        if (step+1) % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss = average_loss / 2000\n",
    "                # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "            cbow_loss_unigram_subsampled.append(average_loss)\n",
    "            print('Average loss at step %d: %f' % (step+1, average_loss))\n",
    "            average_loss = 0\n",
    "            \n",
    "        # Evaluating validation set word similarities\n",
    "        if (step+1) % 10000 == 0:\n",
    "            sim = similarity.eval()\n",
    "            # Here we compute the top_k closest words for a given validation word\n",
    "            # in terms of the cosine distance\n",
    "            # We do this for all the words in the validation set\n",
    "            # Note: This is an expensive step\n",
    "            for i in range(valid_size):\n",
    "                valid_word = reverse_dictionary[valid_examples[i]]\n",
    "                top_k = 8 # number of nearest neighbors\n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
    "                log = 'Nearest to %s:' % valid_word\n",
    "                for k in range(top_k):\n",
    "                    close_word = reverse_dictionary[nearest[k]]\n",
    "                    log = '%s %s,' % (log, close_word)\n",
    "                print(log)\n",
    "    cbow_final_embeddings = normalized_embeddings.eval()\n",
    "    \n",
    "    \n",
    "with open('cbow_unigram_subsampled_losses.csv', 'wt') as f:\n",
    "    writer = csv.writer(f, delimiter=',')\n",
    "    writer.writerow(cbow_loss_unigram_subsampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plotting CBOW (Original), Unigram based Negative Sampling and Subsampling Losses\n",
    "\n",
    "Here we plot the losses obtained over time for all the CBOW based algorithms to see which one performs better. We provide a detailed analysis of the observations in the Chapter 4 text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zHLhbOHcD7nDM3giY1gm2sTxf7O5/oelGIlJbRG7CTs8BezZXuXFGctypp7/H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PJ63qMLgJsz5nPfxb/Fbu6fUCHOrLYu3wk/z3i67axq58MDTeyrc7Gv1kV5Ywe+gNGMdmxGHG98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\ndDMFeAK4KDNXdtT9QQlZr9ZlpK0+twTuB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21ca2381668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pylab.figure(figsize=(15,5))  # in inches\n",
    "\n",
    "# Define the x axis\n",
    "x = np.arange(len(skip_gram_loss))*2000\n",
    "\n",
    "# Plotting standard CBOW loss, CBOW loss with unigram sampling and\n",
    "# CBOW loss with unigram sampling + subsampling here in one plot\n",
    "pylab.plot(x, cbow_loss, label=\"CBOW\",linestyle='--',linewidth=2)\n",
    "pylab.plot(x, cbow_loss_unigram, label=\"CBOW (Unigram)\",linestyle='-.',linewidth=2,marker='^',markersize=5)\n",
    "pylab.plot(x, cbow_loss_unigram_subsampled, label=\"CBOW (Unigram+Subsampling)\",linewidth=2)\n",
    "\n",
    "# Some text around the plots\n",
    "pylab.title('Original CBOW vs Various Improvements Loss Decrease Over-Time',fontsize=24)\n",
    "pylab.xlabel('Iterations',fontsize=22)\n",
    "pylab.ylabel('Loss',fontsize=22)\n",
    "pylab.legend(loc=1,fontsize=22)\n",
    "\n",
    "# Use for saving the figure if needed\n",
    "pylab.savefig('loss_cbow_vs_all_improvements.png')\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
